Data Mining: Concepts and Techniques
AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO. Publisher Diane Cerra Publishing Services Manager Simon Crump Editorial Assistant Asma Stephan Cover Design Cover Image Cover Illustration Text Design Composition diacriTech
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2.4.8 Kullback-Leibler Divergence
hanj.cs.illinois.edufrom an observed frequency distribution, as illustrate in the following example. Example 2.24. Computing the KL Divergence by Smoothing. Sup-pose there are two sample distributions P and Q as follows: P: (a: 3/5,b:
Data Mining: Concepts and Techniques
hanj.cs.illinois.edu3.5 From Data Warehousing to Data Mining 146 3.5.1 Data Warehouse Usage 146 3.5.2 From On-Line Analytical Processing to On-Line Analytical Mining 148 3.6 Summary 150 Exercises 152 Bibliographic Notes 154 Chapter 4 Data Cube Computation and Data Generalization 157 4.1 Efficient Methods for Data Cube Computation 157
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