Introduction to Data Mining: Pearson New International Edition

Introduction to Data Mining: Pearson New International Edition

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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
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Product details

  • Paperback | 736 pages
  • 219 x 276 x 33mm | 1,580g
  • Harlow, United Kingdom
  • English
  • Pearson New International Edition
  • 1292026154
  • 9781292026152
  • 300,676

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





9 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



10 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



11 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 B Dimensionality Reduction

Appendix D Regression

Appendix E Optimization



Author Index

Subject Index
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Rating details

324 ratings
3.98 out of 5 stars
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4 35% (115)
3 18% (58)
2 6% (19)
1 3% (10)
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