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Aug 2001
ISBN 026208290X
425 pp.
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Principles of Data Mining
David J. Hand , Heikki Mannila and Padhraic Smyth

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.

Table of Contents
 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
 
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