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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each

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Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Addison Wesley, 2006. Data Mining Tasks Classification + + +++ ++ + + + + + + + Regression. Clustering Euclidean distance based clustering in 3-D space. Intracluster distances are minimized Intercluster distances are maximized Group points such that – Data points in one cluster are more similar to one another

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14.02.2018Pang-Ning Tan, Michigan State University, Michael Steinbach, University of Minnesota Provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures. Offers instructor resources including solutions for exercises and complete set of lecture slides. Assumes only a modest statistics or mathematics background, and no

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