Introduction To Data Mining Ebook Free Download

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Includes extensive number of integrated examples and figures. Offers instructor resources including solutions for exercises and complete set of lecture slides. Book Description: An introduction to statistical data mining, Data Analysis and Data Mining is both textbook and professional resource. Assuming only a basic knowledge of statistical reasoning, it presents core concepts in data mining and exploratory statistical models to students and professional statisticians-both those working in communications and those working in a technological. This book explores the concepts and techniques of data mining, a promising. Point of view, introducing interesting data mining techniques and systems. AllElectronics and b showing summarized data resulting from drill-down.

Introduction of data mining

[2Wp.eBook] Introduction to Data Mining By Pang-Ning Tan, Michael Steinbach, Vipin Kumar

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Introduction To Mining Pdf

Introduction To Data Mining Ebook Free DownloadDownload


English | 2015 | ISBN: 1938549384 | 333 Pages | PDF | 26 MB
Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc.
Features:
Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis
Discusses the related applications of statistic, e.g., Ward's method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.)
Contains separate chapters on JAN and the clustering of categorical data
Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.
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Introduction To Data Mining Pdf