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Introduction to Data Mining
Pang-Ning Tan, Michigan State University
Michael Steinbach, University of Minnesota
Vipin Kumar, University of Minnesota

ISBN-10: 0321321367
ISBN-13:  9780321321367

Publisher:  Addison-Wesley
Copyright:  2006
Format:  Cloth; 769 pp
Published:  05/02/2005
Status: Instock


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Description

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 data mining technique, followed by more advanced concepts and algorithms.

 

 

Quotes

This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.

-Sanjay Ranka, University of Florida

 

In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules).

-Mohammed Zaki, Rensselaer Polytechnic Institute


Features

  • 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 database knowledge is needed.
  • Topics covered include; predictive modeling, association analysis, clustering, anomaly detection, visualization.


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 Quality

2.3 Data Preprocessing

2.4 Measures of Similarity and Dissimilarity

2.5 Bibliographic Notes

2.6 Exercises

 

3 Exploring Data

3.1 The Iris Data Set 

3.2 Summary Statistics

3.3 Visualization

3.4 OLAP and Multidimensional Data Analysis

3.5 Bibliographic Notes

3.6 Exercises

 

4 Classification: Basic Concepts, Decision Trees, and Model Evaluation

4.1 Preliminaries

4.2 General Approach to Solving a Classification Problem

4.3 Decision Tree Induction

4.4 Model Overfitting

4.5 Evaluating the Performance of a Classifier

4.6 Methods for Comparing Classifiers

4.7 Bibliographic Notes

4.8 Exercises

 

5 Classification: Alternative Techniques

5.1 Rule-Based Classifier

5.2 Nearest-Neighbor Classifiers

5.3 Bayesian Classifiers

5.4 Artificial Neural Network (ANN)

5.5 Support Vector Machine (SVM)

5.6 Ensemble Methods

5.7 Class Imbalance Problem

5.8 Multiclass Problem

5.9 Bibliographic Notes

5.10 Exercises

 

6 Association Analysis: Basic Concepts and Algorithms

6.1 Problem Definition

6.2 Frequent Itemset Generation

6.3 Rule Generation

6.4 Compact Representation of Frequent Itemsets

6.5 Alternative Methods for Generating Frequent Itemsets

6.6 FP-Growth Algorithm

6.7 Evaluation of Association Patterns

6.8 Effect of Skewed Support Distribution

6.9 Bibliographic Notes

6.10 Exercises

 

7 Association Analysis: Advanced Concepts  

7.1 Handling Categorical Attributes

7.2 Handling Continuous Attributes

7.3 Handling a Concept Hierarchy

7.4 Sequential Patterns

7.5 Subgraph Patterns

7.6 Infrequent Patterns

7.7 Bibliographic Notes

7.8 Exercises

 

8 Cluster Analysis: Basic Concepts and Algorithms

8.1 Overview

8.2 K-means

8.3 Agglomerative Hierarchical Clustering

8.4 DBSCAN

8.5 Cluster Evaluation

8.6 Bibliographic Notes

8.7 Exercises

 

9 Cluster Analysis: Additional Issues and Algorithms

9.1 Characteristics of Data, Clusters, and Clustering Algorithms

9.2 Prototype-Based Clustering

9.3 Density-Based Clustering

9.4 Graph-Based Clustering

9.5 Scalable Clustering Algorithms

9.6 Which Clustering Algorithm?

9.7 Bibliographic Notes

9.8 Exercises

 

10 Anomaly Detection

10.1 Preliminaries

10.2 Statistical Approaches

10.3 Proximity-Based Outlier Detection

10.4 Density-Based Outlier Detection

10.5 Clustering-Based Techniques

10.6 Bibliographic Notes

10.7 Exercises

 

Appendix A Linear Algebra

Appendix B Dimensionality Reduction

Appendix C Probability and Statistics

Appendix D Regression

Appendix E Optimization

 

Author Index

Subject Index



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Companion Website for Introduction to Data Mining
Tan, Steinbach & Kumar
©2006  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 0321335678  |  ISBN-13: 9780321335678
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Online Instructor Solutions Manual
Tan, Steinbach & Kumar
©2006  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 0321335651  |  ISBN-13: 9780321335654

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PowerPoint Slides
Tan, Steinbach & Kumar
©2006  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 032133566X  |  ISBN-13: 9780321335661

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Companion Website for Introduction to Data Mining
Tan, Steinbach & Kumar
©2006  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 0321335678  |  ISBN-13: 9780321335678
More Info

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Websites and online courses

Companion Website for Introduction to Data Mining
Tan, Steinbach & Kumar
©2006  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 0321335678  |  ISBN-13: 9780321335678
More Info

CS Support-Student Support Material
Addison-Wesley
©2008  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 0321446852  |  ISBN-13: 9780321446855
More Info


Websites and Online Courses

Companion Website for Introduction to Data Mining
Tan, Steinbach & Kumar
©2006  |  Addison-Wesley  |  On-line Supplement  |  Live
ISBN-10: 0321335678  |  ISBN-13: 9780321335678
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