Principles of Data Mining

Front Cover
MIT Press, 2001 - Computers - 546 pages
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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.

 

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Contents

Introduction
1
Measurement and Data
25
Visualizing and Exploring Data
53
Data Analysis and Uncertainty
93
A Systematic Overview of Data Mining Algorithms
141
Models and Patterns
165
Score Functions for Data Mining Algorithms
211
Search and Optimization Methods
235
Predictive Modeling for Classification
327
Predictive Modeling for Regression
367
Data Organization and Databases
399
Finding Patterns and Rules
427
Retrieval by Content
449
Random Variables
485
References
491
Index
525

Descriptive Modeling
271

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