Principles of data mining

The growing interest in data mining is motivated by a common problem across disciplines- how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Full Contents

List of Tables

List of Figures

Series Foreword

Preface

1 Introduction

2 Measurement and Data

3 Visualizing and Exploring Data

4 Data Analysis and Uncertainty

5 A Systematic Overview of Data Mining Algorithms

6 Models and Patterns

7 Score Functions for Data Mining Algorithms

8 Search and Optimization Methods

9 Descriptive Modeling

10 Predictive Modeling for Classification

11 Predictive Modeling for Regression

12 Data Organization and Databases

13 Finding Patterns and Rule

14 Retrieval by Content

Appendix: Random Variables

References

Index

Available:*

On Order

Summary

Summary

Table of Contents