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Data: The data chapter has been updated to include discussions of mutual information and kernel-based techniques. Exploring Data: The data exploration chapter has been removed from the print edition of the book, but is available on the web. Appendices: web.

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Introduction to Data Mining (First Edition) Pang-Ning Tan, Michigan State University, Michael Steinbach, University of Minnesota Vipin Kumar, University of Minnesota Table of Contents Sample Chapters Resources for Instructors and Students

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1 Introduction 1. Discuss whether or not each of the following activities is a data mining task. (a) Dividing the customers of a company according to their gender. No. This is a simple database query. (b) Dividing the customers of a company according to their prof

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1/1/2005· Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

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Basically, this book is a very good introduction book for data mining. It discusses all the main topics of data mining that are clustering, classification, pattern mining, and outlier detection.Moreover, it contains two very good chapters on clustering by Tan & Kumar.

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We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good stone covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection.

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Table of Contents 1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data

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We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good stone covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection.

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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.

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Introduction to Data Mining by Pang-Ning Tan, 9780133128901, available at Book Depository with free delivery worldwide. Introduction to Data Mining, Second Edition, is intended for use in the Data Mining course. It is also suitable for individuals seeking an

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1/1/2005· 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

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We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good stone covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection.

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Introduction to Data Mining (豆瓣)

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.

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