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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.

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

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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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