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K-means clustering: how it works
 
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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 420387 Victor Lavrenko
What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning & explanation
 
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What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals. Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.
Views: 5298 The Audiopedia
K means clustering using python
 
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The scikit learn library for python is a powerful machine learning tool. K means clustering, which is easily implemented in python, uses geometric distance to create centroids around which our data can fit as clusters. In the example attached to this article, I view 99 hypothetical patients that are prompted to sync their smart watch healthcare app data with a research team. The data is recorded continuously, but to comply with healthcare regulations, they have to actively synchronize the data. This example works equally well is we consider 99 hypothetical customers responding to a marketing campaign. In order to prompt them, several reminder campaigns are run each year. In total there are 32 campaigns. Each campaign consists only of one of the following reminders: e-mail, short-message-service, online message, telephone call, pamphlet, or a letter. A record is kept of when they sync their data, as a marker of response to the campaign. Our goal is to cluster the patients so that we can learn which campaign type they respond to. This can be used to tailor their reminders for the next year. In the attached video, I show you just how easy this is to accomplish in python. I use the python kernel in a Jupyter notebook. There will also a mention of dimensionality reduction using principal component separation, also done using scikit learn. This is done so that we can view the data as a scatter plot using the plotly library.
Views: 25116 Juan Klopper
Machine learning with Python and sklearn - Hierarchical Clustering (E-commerce dataset example)
 
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In this Machine Learning & Python video tutorial I demonstrate Hierarchical Clustering method. Hierarchical Clustering is a part of Machine Learning and belongs to Clustering family: - Connectivity-based clustering (hierarchical clustering) - Centroid-based clustering (K-Means Clustering) - https://www.youtube.com/watch?v=iybATqk6LNI - Distribution-based clustering - Density-based clustering In data mining and statistics, Hierarchical Clustering also called hierarchical cluster analysis or HCA is a method of cluster analysis which seeks to build a hierarchy of clusters. In this video I demonstrate how Agglomerative Hierarchical Clustering is working. Must know for Hierarchical Clustering is knowing Dendrograms. Dendrogram helps you to decide the optimal number of clusters for your dataset. For executing task in Python I used: - sklearn library that is for Machine Learning algorithms. - ward method that means Minimum Variance Method. If you are interesting more in Hierarchical Clustering, read my article on LinkedIn where I described my experiment about combining Machine Learning (Hierarchical Clustering) in GIS (Geographical Information System). - https://www.linkedin.com/pulse/machine-learning-gis-hierarchical-clustering-urban-bielinskas Data-set for this example is taken from https://www.kaggle.com. There you can find many dataset for very different Machine Learning tasks. Hierarchicaal Clustering is very usable in solving Data Analysis, Data Mining and Statistics problems. If you have any question or comments please write below. Do not forget to subscribe me if want to follow my new videos about Machine Learning, Data Science, Python programming and relative issues. Follow me on LinkedIn: https://www.linkedin.com/in/bielinskas/
Views: 2041 Vytautas Bielinskas
Article Review: A Hybrid Monkey Search Algorithm for Clustering Analysis - Chen et. al.
 
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Monkey search is a novel meta-heuristic that emerged in 2007 as a searching technique. This paper was published in 2014 by Chen et. all which utilized Artificial Bee Colony algorithm's k-means clustering move operator to implement a simulated monkey somersault. This avoids convergence on a local optima. The papers attempts to compare it with other comparable algorithms and establishes the algorithm is consistent, reliable and efficient for complex, non-differentible, non-linear, multi-modal and high dimensional optimization problems.
Views: 186 Rajat Dhiman
Dimensionality Reduction - The Math of Intelligence #5
 
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Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis. Code for this video: https://github.com/llSourcell/Dimensionality_Reduction Ong's Winning Code: https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps Hammad's Runner up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization Please Subscribe! And like. And comment. That's what keeps me going. I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ https://www.youtube.com/watch?v=lrHboFMio7g https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/principal-component-analysis/ http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 55009 Siraj Raval
What is CONSTRAINED CLUSTERING? What does CONSTRAINED CLUSTERING mean?
 
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What is CONSTRAINED CLUSTERING? What does CONSTRAINED CLUSTERING mean? CONSTRAINED CLUSTERING meaning - CONSTRAINED CLUSTERING definition - CONSTRAINED CLUSTERING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both, with a Data clustering algorithm. Both a must-link and a cannot-link constraint define a relationship between two data instances. A must-link constraint is used to specify that the two instances in the must-link relation should be associated with the same cluster. A cannot-link constraint is used to specify that the two instances in the cannot-link relation should not be associated with the same cluster. These sets of constraints acts as a guide for which a constrained clustering algorithm will attempt to find clusters in a data set which satisfy the specified must-link and cannot-link constraints. Some constrained clustering algorithms will abort if no such clustering exists which satisfies the specified constraints. Others will try to minimize the amount of constraint violation should it be impossible to find a clustering which satisfies the constraints. Constraints could also be used to guide the selection of a clustering model among several possible solutions. A cluster in which the members conform to all must-link and cannot-link constraints is called a chunklet.
Views: 288 The Audiopedia
Unsupervised Machine Learning with DBSCAN
 
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Unsupervised Machine Learning with DBSCAN Become a Patron and support this channel:- https://www.patreon.com/user?u=9926749 Description and References:- In this video, we fit the DBSCAN model to the color data frame we created in the previous video. With DBSCAN we are doing unsupervised machine learning as we are not using training data. We then ascertain how well the DBSCAN model fits the data. Part 1 to 4, “Reading an Excel spreadsheet with Pandas”:- https://www.youtube.com/watch?v=2M6gP1foFxg&list=PLEHiyJfYrr_R3IDOUsf6yXJrQHjtWQayb&index=5 Note the parameters h1, h2, h3 and h4 were renamed to col_desc_row_0, col_desc_row_1, col_desc_row_2, col_desc_row_3 and col_desc_row_4 in the current video for clarity. Pandas Link:- http://pandas.pydata.org/talks.html http://pandas.pydata.org/pandas-docs/stable/ Matplotlib Citation:- @Article{Hunter:2007, Author = {Hunter, J. D.}, Title = {Matplotlib: A 2D graphics environment}, Journal = {Computing In Science \& Engineering}, Volume = {9}, Number = {3}, Pages = {90--95}, abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.}, publisher = {IEEE COMPUTER SOC}, doi = {10.1109/MCSE.2007.55}, year = 2007 } Spyder 3 Link:- https://pythonhosted.org/spyder/ Make blobs reference http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs The scikit-learn references for DBSCAN:- http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py
Views: 1099 Python Statistical
Data Mining: How You're Revealing More Than You Think
 
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Data mining recently made big news with the Cambridge Analytica scandal, but it is not just for ads and politics. It can help doctors spot fatal infections and it can even predict massacres in the Congo. Hosted by: Stefan Chin Head to https://scishowfinds.com/ for hand selected artifacts of the universe! ---------- Support SciShow by becoming a patron on Patreon: https://www.patreon.com/scishow ---------- Dooblydoo thanks go to the following Patreon supporters: Lazarus G, Sam Lutfi, Nicholas Smith, D.A. Noe, سلطان الخليفي, Piya Shedden, KatieMarie Magnone, Scott Satovsky Jr, Charles Southerland, Patrick D. Ashmore, Tim Curwick, charles george, Kevin Bealer, Chris Peters ---------- Looking for SciShow elsewhere on the internet? Facebook: http://www.facebook.com/scishow Twitter: http://www.twitter.com/scishow Tumblr: http://scishow.tumblr.com Instagram: http://instagram.com/thescishow ---------- Sources: https://www.aaai.org/ojs/index.php/aimagazine/article/viewArticle/1230 https://www.theregister.co.uk/2006/08/15/beer_diapers/ https://www.theatlantic.com/technology/archive/2012/04/everything-you-wanted-to-know-about-data-mining-but-were-afraid-to-ask/255388/ https://www.economist.com/node/15557465 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/ https://qz.com/584287/data-scientists-keep-forgetting-the-one-rule-every-researcher-should-know-by-heart/ https://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853 http://dml.cs.byu.edu/~cgc/docs/mldm_tools/Reading/DMSuccessStories.html http://content.time.com/time/magazine/article/0,9171,2058205,00.html https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf https://www.cs.helsinki.fi/u/htoivone/pubs/advances.pdf http://cecs.louisville.edu/datamining/PDF/0471228524.pdf https://bits.blogs.nytimes.com/2012/03/28/bizarre-insights-from-big-data https://scholar.harvard.edu/files/todd_rogers/files/political_campaigns_and_big_data_0.pdf https://insights.spotify.com/us/2015/09/30/50-strangest-genre-names/ https://www.theguardian.com/news/2005/jan/12/food.foodanddrink1 https://adexchanger.com/data-exchanges/real-world-data-science-how-ebay-and-placed-put-theory-into-practice/ https://www.theverge.com/2015/9/30/9416579/spotify-discover-weekly-online-music-curation-interview http://blog.galvanize.com/spotify-discover-weekly-data-science/ Audio Source: https://freesound.org/people/makosan/sounds/135191/ Image Source: https://commons.wikimedia.org/wiki/File:Swiss_average.png
Views: 132749 SciShow
What is DATA STREAM MINING? What does DATA STREAM MINING mean? DATA STREAM MINING meaning
 
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What is DATA STREAM MINING? What does V mean? DATA STREAM MINING meaning - DATA STREAM MINING definition - DATA STREAM MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.
Views: 320 The Audiopedia
Moore Methods - Text and Data Mining (2017 update)
 
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Researchers often have to go through lots of articles and papers to find key information for their own work. This can take quite a long time but what if there was a method that could help? In this video, we give an overview of Text and Data Mining (TDM). TDM is an interesting technique that can help with analysing text and other information quickly, allowing you to get results and get on with your work. Want to take things further? Check out our blog for more learning opportunities and activities: https://23researchthingscam.wordpress.com/2016/11/23/thing-19-text-and-data-mining/
Views: 205 Moore Library
THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING
 
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Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 28 aircc journal
Text Categorization and Clustering Data Mining Rapidminer Projects
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/ http://www.phdprojects.org/phd-research-topic-wireless-body-area-network/
Views: 4880 PHD Projects
Implementation of DBSCAN algorithm and comparing with Kmeans algorithm
 
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This tutorial is about 'Implementation of DBSCAN algorithm and comparing with Kmeans algorithm'. A correction from video: Please replace the word 'Homogeneity' by 'Purity'. In this tutorial, I tried to explain some important concepts like: 1. How to determine 'eps' value for a given dataset. 2. How to calculate purity of a cluster. One thing I din't mention in the tutorial. The value of minPts depends on how many clusters you want to generate. Let's say if you want to generate big clusters and less number of clusters then set minPts value high. Too low value of minPts leads to generate more clusters from noise points so try to avoid setting minPts value too low. High or low value for minPts is relative and strongly depends on the size of the dataset. Find the 'optimal epsilon (Eps) value' paper here: http://iopscience.iop.org/article/10.1088/1755-1315/31/1/012012/pdf Find details about Normalization here: https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range
Views: 1025 ScoobyData Doo
What Is Meant By Classifier In Data Mining?
 
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Br data analysis task is an example of numeric prediction, where 11 feb 2017 all classification techniques assume some knowledge the. Edudata mining evaluation of classifiers. Once a datification scheme has been created, security standards that specify appropriate handling practices for each category and storage define the data's lifecyle requirements should be mining classificationwhat is classification? What prediction? Issues regarding prediction set t split into two subsets t1 t2 with sizes n1 n2 respectively, gini index of contains examples from n classes, gini(t) defined as. Data mining classification what is classification? Usual examples and regression data with weka, part 2 clustering ibm. Classification and prediction nyu computer science. Given a database of tuples and set classes, the classification problem is to define mapping where each tuple objective analyze input data develop an accurate description or model for class using features present in. Binary4 example6 probabilities8 data structure. The process of identifying the relationship and effects this on outcome future values objects is defined as regression. By simple definition, in classification clustering analyze a set of data and generate grouping rules which can be used to typically the learning task like any mining is an iterative process proaches, algorithm settings, before good classifier found. Classification is a two step process. A study on classification techniques in data mining ieee xplore. Classifiers for educational data mining semantic scholar. What's an example of this its simplicity means it's generally faster and more efficient than other algorithms, especially over large datasets. Regression helps in identifying 11 may 2010 this second article of the series, we'll discuss two common data mining methods classification and clustering which can be used to do more powerful you could have best about your customers (whatever that even means), but if don't apply right models it, it will just garbage abstract is a process inferring knowledge from such huge. Data mining classification and prediction slideshare. A classifier is a tool in data mining that takes bunch of representing things we want to classify and attempts predict which class the new belongs. Classification in data mining eecs. Data mining classification & prediction tutorialspoint. 04 classification in data mining slideshare. What is datification? Definition from whatis. The term 'classifier' sometimes also refers to the mathematical function, implemented by a therefore, 80. Data mining has three major components clustering or classification, association rules and sequence analysis. Mean absolute error and other coefficient. Poznan, poland mean squared error. Do not hesitate to ask any questions or read books!. Top 10 data mining algorithms, explained kdnuggets. Branches are added by making the same information gain calculation for data defined location on tree of classification can be applied to simpl
Views: 34 Roselyn Wnuk Tipz
mod01lec01
 
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Views: 17153 Data Mining - IITKGP
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Views: 127370 Siraj Raval
Joint Cluster Analysis of Attribute Data and Relationship Data: Problems, Algorithms & Applications
 
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Attribute data and relationship data are two principle types of data, representing the intrinsic and extrinsic properties of entities. While attribute data has been the main source of data for cluster analysis, relationship data such as social networks or metabolic networks are becoming increasingly available. In many cases these two data types carry complementary information, which calls for a joint cluster analysis of both data types in order to achieve more natural clusterings. For example, when identifying research communities, relationship data could represent co-author relationships and attribute data could represent the research interests of scientists. Communities could then be identified as clusters of connected scientists with similar research interests. Our introduction of joint cluster analysis is part of a recent, broader trend to consider as much background information as possible in the process of cluster analysis, and in general, in data mining. In this talk, we briefly review related work including constrained clustering, semi-supervised clustering and multi-relational clustering. We then propose the Connected k-Center (CkC) problem, which aims at finding k connected clusters minimizing the radius with respect to the attribute data. We sketch the main ideas of the proof of NP-completeness and present a constant factor approximation algorithm for the CkC problem. Since this algorithm does not scale to large datasets, we have also developed NetScan, a heuristic algorithm that is efficient for large, real databases. We report experimental results from two applications, community identification and document clustering, both based on DBLP data. Our experiments demonstrate that NetScan finds clusters that are more meaningful and accurate than the results of existing algorithms. We conclude the talk with other promising applications and new problems of joint cluster analysis. In particular, we discuss the clustering of gene expression data and the hotspot analysis of crime data as well as a joint cluster analysis problem that does not require the user to specify the number of clusters in advance.
Views: 39 Microsoft Research
Techniques for random sampling and avoiding bias | Study design | AP Statistics | Khan Academy
 
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Techniques for random sampling and avoiding bias. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/gathering-data-ap/sampling-methods/v/techniques-for-random-sampling-and-avoiding-bias?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics. Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc
Views: 73536 Khan Academy
Mining image data to better characterize cancer
 
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Freely access the full article here: https://www.nature.com/articles/srep11044/?utm_source=Youtube&utm_medium=Video&utm_content=Multidisciplinary-Scientific_Reports-Multidisciplinary-Usage_driving-Global&utm_campaign=MatAst-Nature-youtube_noncfp_content Chintan Parmar, Patrick Grossmann, Johan Bussink, Philippe Lambin & Hugo J. W. L. Aerts. "Machine Learning methods for Quantitative Radiomic Biomarkers". Scientific Reports 2015. https://www.nature.com/articles/srep11044/?utm_source=Youtube&utm_medium=Video&utm_content=Multidisciplinary-Scientific_Reports-Multidisciplinary-Usage_driving-Global&utm_campaign=MatAst-Nature-youtube_noncfp_content Scientific Reports is the open access home for all science that is methodologically, analytically and ethically robust, as judged by a rigorous peer review process. Find the latest articles from Scientific Reports: http://go.nature.com/sreparticles More information on Scientific Reports at http://go.nature.com/srepabout Find out about publishing with Scientific Reports: http://go.nature.com/sreppublishing Twitter: http://go.nature.com/SciRepTwit Facebook: http://go.nature.com/SciRepFB
Views: 327 Scientific Reports
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 5013 The Audiopedia
What is DOCUMENT CLUSTERING? What does DOCUMENT CLUSTERING mean? DOCUMENT CLUSTERING meaning
 
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What is DOCUMENT CLUSTERING? What does DOCUMENT CLUSTERING mean? DOCUMENT CLUSTERING meaning - DOCUMENT CLUSTERING definition - DOCUMENT CLUSTERING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering. Document clustering involves the use of descriptors and descriptor extraction. Descriptors are sets of words that describe the contents within the cluster. Document clustering is generally considered to be a centralized process. Examples of document clustering include web document clustering for search users. The application of document clustering can be categorized to two types, online and offline. Online applications are usually constrained by efficiency problems when compared to offline applications. In general, there are two common algorithms. The first one is the hierarchical based algorithm, which includes single link, complete linkage, group average and Ward's method. By aggregating or dividing, documents can be clustered into hierarchical structure, which is suitable for browsing. However, such an algorithm usually suffers from efficiency problems. The other algorithm is developed using the K-means algorithm and its variants. Generally hierarchical algorithms produce more in-depth information for detailed analyses, while algorithms based around variants of the K-means algorithm are more efficient and provide sufficient information for most purposes.:Ch.14 These algorithms can further be classified as hard or soft clustering algorithms. Hard clustering computes a hard assignment – each document is a member of exactly one cluster. The assignment of soft clustering algorithms is soft – a document’s assignment is a distribution over all clusters. In a soft assignment, a document has fractional membership in several clusters.:499 Dimensionality reduction methods can be considered a subtype of soft clustering; for documents, these include latent semantic indexing (truncated singular value decomposition on term histograms) and topic models. Other algorithms involve graph based clustering, ontology supported clustering and order sensitive clustering. Given a clustering, it can be beneficial to automatically derive human-readable labels for the clusters. Various methods exist for this purpose.
Views: 1133 The Audiopedia
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
 
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This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python. The topics covered in this Naive Bayes video are as follows: 1. What is Naive Bayes? ( 01:06 ) 2. Naive Bayes and Machine Learning ( 05:45 ) 3. Why do we need Naive Bayes? ( 05:46 ) 4. Understanding Naive Bayes Classifier ( 06:30 ) 5. Advantages of Naive Bayes Classifier ( 20:17 ) 6. Demo - Text Classification using Naive Bayes ( 22:36 ) To learn more about Machine Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slides here: https://goo.gl/Cw9wqy #NaiveBayes #MachineLearningAlgorithms #DataScienceCourse #DataScience #SimplilearnMachineLearning - - - - - - - - Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer Why learn Machine Learning? Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems The Machine Learning Course is recommended for: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning Learn more at: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Naive-Bayes-Classifier-l3dZ6ZNFjo0&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 11479 Simplilearn
Hierarchical clustering
 
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In data mining, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 3213 Audiopedia
Hybrid Cluster Demo
 
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Watch Hybrid Cluster features: Auto Scaling, Point-in-time Restore and Self Healing
Views: 3619 hybridsites
What is INCREMENTAL LEARNING? What does INCREMENTAL LEARNING mean? INCREMENTAL LEARNING meaning
 
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What is INCREMENTAL LEARNING? What does INCREMENTAL LEARNING mean? INCREMENTAL LEARNING meaning - INCREMENTAL LEARNING definition - INCREMENTAL LEARNING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computer science, incremental learning is a method of machine learning, in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms. Many traditional machine learning algorithms inherently support incremental learning, other algorithms can be adapted to facilitate this. Examples of incremental algorithms include decisions trees (IDE4, ID5R), decision rules, artificial neural networks (RBF networks, Learn++, Fuzzy ARTMAP, TopoART, and IGNG) or the incremental SVM. The aim of incremental learning is for the learning model to adapt to new data without forgetting its existing knowledge, it does not retrain the model. Some incremental learners have built-in some parameter or assumption that controls the relevancy of old data, while others, called stable incremental machine learning algorithms, learn representations of the training data that are not even partially forgotten over time. Fuzzy ART and TopoART are two examples for this second approach. Incremental algorithms are frequently applied to data streams or big data, addressing issues in data availability and resource scarcity respectively. Stock trend prediction and user profiling are some examples of data streams where new data becomes continuously available. Applying incremental learning to big data aims to produce faster classification or forecasting times.
Views: 919 The Audiopedia
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 156538 Timothy DAuria
Data Mining
 
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Engineers explain data mining concepts giving commonly used techniques and methods according to: "Top 10 Algorithms in Data Mining" by XindongWu · Vipin Kumar · J. Ross Quinlan · Joydeep Ghosh · Qiang Yang · Hiroshi Motoda · Geoffrey J. McLachlan · Angus Ng · Bing Liu · Philip S. Yu · Zhi-Hua Zhou · Michael Steinbach · David J. Hand · Dan Steinberg 9 July 2007 UCLA article: http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm Song: Miles Davis "So What" Kind of Blue (1959)
Views: 26 Nick Losee
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 5622 BookBd
What is AUDIO MINING? What does AUDIO MINING mean? AUDIO MINING meaning, definition & explanation
 
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What is AUDIO MINING? What does AUDIO MINING mean? AUDIO MINING meaning - AUDIO MINING definition - AUDIO MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Audio mining is a technique by which the content of an audio signal can be automatically analysed and searched. It is most commonly used in the field of automatic speech recognition, where the analysis tries to identify any speech within the audio. The audio will typically be processed by a speech recognition system in order to identify word or phoneme units that are likely to occur in the spoken content. This information may either be used immediately in pre-defined searches for keywords or phrases (a real-time "word spotting" system), or the output of the speech recogniser may be stored in an index file. One or more audio mining index files can then be loaded at a later date in order to run searches for keywords or phrases. The results of a search will normally be in terms of hits, which are regions within files that are good matches for the chosen keywords. The user may then be able to listen to the audio corresponding to these hits in order to verify if a correct match was found. Audio mining systems used in the field of speech recognition are often divided into two groups: those that use Large Vocabulary Continuous Speech Recognisers (LVCSR) and those that use phonetic recognition. Musical audio mining (also known as music information retrieval) relates to the identification of perceptually important characteristics of a piece of music such as melodic, harmonic or rhythmic structure. Searches can then be carried out to find pieces of music that are similar in terms of their melodic, harmonic and/or rhythmic characteristics.
Views: 211 The Audiopedia
International Journal of Data Mining & Knowledge Management Process ( IJDKP )
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects,surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining,Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining. Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks,Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing,OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper submission Authors are invited to submit papers for this journal through e-mail: ijdkpjournal@airccse.org. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 47 aircc journal
How To Do Massive Data Machine Learning -- eHarmony Tech Talk with Alex Gray
 
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About This Talk: We'll begin by looking at seven basic types of machine learning problems, such as classification and clustering, and the best methods used to solve them, which include support vector machines, nearest-neighbors, principal component analysis, hierarchical clustering, kernel density estimation, Nadaraya-Watson regression, kernel conditional density estimation, Gaussian process regression, manifold learning, n-point correlation functions, minimum spanning trees, nonparametric Bayes classifiers, and others. I will then describe how we can scale each of these methods to work on massive datasets, despite their often quadratic or cubic scaling with the number of data, via seven different types of computational techniques. Finally, I'll describe the recently-announced first professional-grade machine learning system that incorporates the techniques described, called Skytree Server. Alexander Gray, PhD CTO, Skytree Inc. Associate Professor, Georgia Institute of Technology About Alexander Gray: Alexander Gray received bachelor's degrees in Applied Mathematics and Computer Science from the University of California, Berkeley and a PhD in Computer Science from Carnegie Mellon University, and is currently an Associate Professor in the College of Computing at Georgia Tech. His research group, the FASTlab, aims to comprehensively scale up all of the major practical methods of machine learning to massive datasets as well as develop new statistical methodology and theory, and has developed a number of the current fastest algorithms for several key problems. He began working with massive scientific datasets in 1993 (long before the current fashionable talk of "big data") at NASA's Jet Propulsion Laboratory in its Machine Learning Systems Group. High-profile applications of his large-scale ML algorithms have been described in staff written articles in Science and Nature, including contributions to work selected by Science as the Top Scientific Breakthrough of 2003. He has won or been nominated for a number of best paper awards in statistics and data mining and is a recipient of the National Science Foundation CAREER Award. He is a national authority on the topic of big-data machine learning, giving invited tutorial lectures on massive-scale data analysis at the top data analysis research conferences, government agencies, and corporations, and serving on the National Academy of Sciences Committee on the Analysis of Massive Data. About Skytree, Inc.: Skytree® -- The Machine Learning Company® is disrupting the Advanced Analytics market with a Machine Learning platform that gives organizations the power to discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets and customers. Predictive Analytics is quickly becoming a must-have technology in the age of Big Data, and Skytree is at the forefront with enterprise-grade Machine Learning. Skytree's flagship product -- Skytree Server -- is the only general purpose scalable Machine Learning system on the market, built for the highest accuracy at unprecedented speed and scale.
Views: 1758 Skytree, Inc.
Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 81442 Francisco Iacobelli
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
 
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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 4218 The Audiopedia
How to Mind Cluster
 
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This is a video demonstration of how to create your own mind cluster. A mind cluster is a form of brain dump. If you would like to read more about the concept of mind clustering, read our article on Mind Clustering at http://www.cognicology.com/mind-clustering-organize-your-mind-and-get-things-done/
Views: 542 Cognicology
IJDKP - May 2016
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdk... ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/ visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources Paper submission Authors are invited to submit papers for this journal through e-mail ijdkpjournal@airccse.org. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit http://airccse.org/journal/ijdkp/ijdk...
Views: 11 aircc journal
ANITA Lecture - Density Estimation and Clustering - Sanjib Sharma
 
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Title: Density Estimation and Clustering Speaker: Sanjib Sharma, University of Sydney Date: 1pm (AEDT) Wednesday 12th March 2014 Abstract: Estimating density of a given set of points and identifying clusters are two important techniques to reveal hidden information in data. With recent advances in technology, today we have data which is both large (number of objects per data set) and rich (amount of information in each object). This poses a unique challenge for data mining. In multi dimensional spaces, not all the algorithms work equally well. Also, not all algorithms are computationally efficient for analyzing large amounts of data. In this lecture, I will discuss various algorithms and highlight their strengths and weaknesses. I will then concentrate on a few algorithms that work well in multi dimensional spaces and are also fast and efficient to be applied on large data sets. I will also show a few applications of these algorithms to astronomy. Additional Material: Talk slides are available for download at http://goo.gl/D1QMm9.
Views: Anita Chapter
▶ What is Data Mining?  - A Brief Introduction to Data Mining || Data Mining Bangla Tutorial
 
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How data mining Works. Data Mining Definition. What does Data Mining do? »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner সহজ কথায় বিশাল সাইজের গোছালো ও অগোছালো ডেটা থেকে সঠিক ও অন্তর্নিহিত ডেটা বের করে তাদের(ডেটা) মধ্যকার সামঞ্জস্য, প্যাটার্ন, অথবা ইনসাইথস তৈরি করার পদ্ধতি ডেটা মাইনিং নামে পরিচিত। এ ব্যপারটি খনি থেকে স্বর্ণ অথবা রৌপ্য বের করে আনার মত। Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends.
Views: 3188 BookBd
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
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What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 132 The Audiopedia
What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning & explanation
 
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What is STRUCTURE MINING? What does STRUCTURE MINING mean? STRUCTURE MINING meaning - STRUCTURE MINING definition - STRUCTURE MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Structure mining or structured data mining is the process of finding and extracting useful information from semi-structured data sets. Graph mining, sequential pattern mining and molecule mining are special cases of structured data mining. The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data. XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees. Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted. Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty. There is a tacit assumption made in the design of most data mining algorithms that the data presented will be complete. The other necessity is that the actual mining algorithms employed, whether supervised or unsupervised, must be able to handle sparse data. Namely, machine learning algorithms perform badly with incomplete data sets where only part of the information is supplied. For instance methods based on neural networks. or Ross Quinlan's ID3 algorithm. are highly accurate with good and representative samples of the problem, but perform badly with biased data. Most of times better model presentation with more careful and unbiased representation of input and output is enough. A particularly relevant area where finding the appropriate structure and model is the key issue is text mining. XPath is the standard mechanism used to refer to nodes and data items within XML. It has similarities to standard techniques for navigating directory hierarchies used in operating systems user interfaces. To data and structure mine XML data of any form, at least two extensions are required to conventional data mining. These are the ability to associate an XPath statement with any data pattern and sub statements with each data node in the data pattern, and the ability to mine the presence and count of any node or set of nodes within the document. As an example, if one were to represent a family tree in XML, using these extensions one could create a data set containing all the individuals in the tree, data items such as name and age at death, and counts of related nodes, such as number of children. More sophisticated searches could extract data such as grandparents' lifespans etc. The addition of these data types related to the structure of a document or message facilitates structure mining.
Views: 225 The Audiopedia
Robert Meyer - Analysing user comments with Doc2Vec and Machine Learning classification
 
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Description I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Can we determine for a particular user comment from which news site it originated? Abstract Doc2Vec is a nice neural network framework for text analysis. The machine learning technique computes so called document and word embeddings, i.e. vector representations of documents and words. These representations can be used to uncover semantic relations. For instance, Doc2Vec may learn that the word "King" is similar to "Queen" but less so to "Database". I used the Doc2Vec framework to analyze user comments on German online news articles and uncovered some interesting relations among the data. Furthermore, I fed the resulting Doc2Vec document embeddings as inputs to a supervised machine learning classifier. Accordingly, given a particular comment, can we determine from which news site it originated? Are there patterns among user comments? Can we identify stereotypical comments for different news sites? Besides presenting the results of my experiments, I will give a short introduction to Doc2Vec. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 12452 PyData
IJDKP
 
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International Journal of Data Mining & Knowledge Management Process ( IJDKP ) http://airccse.org/journal/ijdkp/ijdkp.html ISSN : 2230 - 9608[Online] ; 2231 - 007X [Print] Call for Papers Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum.Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects,surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining,Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining. Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks,Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing,OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper submission Authors are invited to submit papers for this journal through e-mail: ijdkpjournal@airccse.org. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Views: 16 aircc journal
Mining articles for practical insight for content creation - Łukasz Dziekan, Michał Stolarczyk
 
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Description As a support to our marketing team we have created a tool which analyzes article headlines and contents. It gives insights how to create headlines and models potential "virality" of the content piece, This was particularly challenging because of limited support for NLP in polish language. And it is actually used by our marketing team. Abstract Using Facebook API we have collected data from fanpages of Polish portals publishing articles in the internet. Based on number of shares, comments, likes and other reactions we defined the virality coefficient, which allows us to measure how much potential each article has to become viral, and therefore being particularly interesting in terms of marketing potential. Given this dataset, we wanted to classify the most catchy phrases occurring in article titles and to check if the content actually matters. We examined how these best phrases change over time, did clustering based on their meaning. Moreover, we automated the process of distinguishing between phrases being one-time events (27-1) and those occurring regularly. We also consider impact of other features of the headline on the virality of the article. Additionally we examine the formatting features based on article content and formatting. Higher level virality analysis concerns linking articles covering the same topic, which requires inclusion of our dataset HTML code of article and text (body) extraction out of it. During our speech we will cover the following areas: Data collection: facebook API (headline, article link, reactions) downloading HTML code article text extraction Data preprocessing: stemming tokenization Analysis: token, bigram, trigram, starting and ending phrases frequencies and scores variance and entropy – automatic detection of one-off, regular and seasonal headlines/topics x-validation on different time intervals and using different news-sources virality score vs headline length Analyses : all of the above analyses for article text and HTML code topic analysis (LDA) Modeling: ensemble modeling to for regression algorithms/classification algorithms to predict virality www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1112 PyData
K-means clustering
 
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k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1092 Audiopedia
Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn
 
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This Linear Regression in Machine Learning video will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning tutorial is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms. Below topics are covered in this Linear Regression Machine Learning Tutorial: 1. Introduction to Machine Learning 2. Machine Learning Algorithms 3. Applications of Linear Regression 4. Understanding Linear Regression 5. Multiple Linear Regression 6. Usecase - Profit estimation of companies What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Linear-Regression-NUXdtN1W1FE&utm_medium=Tutorials&utm_source=youtube #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? We recommend this Machine Learning training course for the following professionals in particular: 1. Developers aspiring to be a data scientist or Machine Learning engineer 2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence 4. Graduates looking to build a career in data science and Machine Learning - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 17017 Simplilearn
Candidate Generation - Chapter 4 Part 2
 
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Text Mining and Analytics Candidate Generation - Chapter 4 Part 2 This video tutorials cover major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications. analytics | analytics tools | analytics software | data analysis programs | data mining tools | data mining | text analytics | strucutred data | unstructured data |text mining | what is text mining | text mining techniques | AQL | Annotation Query Language More Articles, Scripts and How-To Papers on http://www.aodba.com
Views: 143 AO DBA
Visual Classification, Cluster Review, and Document Typing
 
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42 seconds to understand how visual classification works in information governance.
Views: 1166 BeyondRecognition
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 98538 LearnEveryone