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Data Mining: Concepts and Techniques

Data mining : Concepts and TechniquesData mining : Concepts and TechniquesSecond EditionJiawei HanandMicheline KamberUniversity of Illinois at Urbana-ChampaignA M S T E R D A M B O S T O NH E I D E L B E R G L O N D O NN E W Y O R K O X F O R D P A R I SS A N D I E G O S A N F R A N C I S C OS I N G A P O R E S Y D N E Y T O K Y OPublisherDiane CerraPublishingServices Manager Simon CrumpEditorial AssistantAsma StephanCover DesignCover ImageCover IllustrationText DesignCompositiondiacriTechTechnical IllustrationDartmouth Publishing, PressProofreaderMultiscience PressIndexerMultiscience PressInterior printerMaple-Vail Book Manufacturing GroupCover printerPhoenix ColorMorgan Kaufmann Publishers is an imprint of Sansome Street, Suite 400, San Francisco, CA 94111 This book is printed on acid-free 2006 by Elsevier Inc.

1.4.2 Mining Frequent Patterns, Associations, and Correlations 23 1.4.3 Classification and Prediction 24 1.4.4 Cluster Analysis 25 1.4.5 Outlier Analysis 26 1.4.6 Evolution Analysis 27 1.5 Are All of the Patterns Interesting? 27 1.6 Classification of Data Mining Systems 29 1.7 Data Mining Task Primitives 31 1.8 Integration of a Data Mining ...

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Data Mining: Concepts and Techniques

hanj.cs.illinois.edu

3.5 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

  Data, Mining, Data mining

2.4.8 Kullback-Leibler Divergence

hanj.cs.illinois.edu

from 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:

  Distribution, Frequency, Divergence, 8 kullback leibler divergence, Kullback, Leibler

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