Principles of Data Mining

by David J. Hand, Heikki Mannila and Padhraic Smyth

• ISBN13: 9780262082907
• ISBN10: 026208290X
• Format: Hardcover
• Copyright: 2001-08-01
• Publisher: Bradford Books

Summary

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.

Author Biography

David Hand is Professor of Statistics, Department of Mathematics, Imperial College, London.

Table of Contents

Full Contents

List of Tables

List of Figures

Series Foreword

Preface

Introduction

Measurement and Data

Visualizing and Exploring Data

Data Analysis and Uncertainty

A Systematic Overview of Data Mining Algorithms

Models and Patterns

Score Functions for Data Mining Algorithms

Search and Optimization Methods

Descriptive Modeling

Predictive Modeling for Classification

Predictive Modeling for Regression

Data Organization and Databases

Finding Patterns and Rule

Retrieval by Content

Appendix: Random Variables

References

Index

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