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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. All rights used by companies to distinguish their products are often claimed as trademarks orregistered trademarks.

6.15.2 ROC Curves 372 6.16 Summary 373 Exercises 375 Bibliographic Notes 378 Chapter 7 Cluster Analysis 383 7.1 What Is Cluster Analysis? 383 7.2 Types of Data in Cluster Analysis 386 7.2.1 Interval-Scaled Variables 387 7.2.2 Binary Variables 389 7.2.3 Categorical, Ordinal, and Ratio-Scaled Variables 392 7.2.4 Variables of Mixed Types 395 7.2.5 ...

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Transcription of Data Mining: Concepts and Techniques

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Data Mining: Concepts and Techniques

hanj.cs.illinois.edu

2.4 Data Integration and Transformation 67 2.4.1 Data Integration 67 2.4.2 Data Transformation 70 2.5 Data Reduction 72 2.5.1 Data Cube Aggregation 73 2.5.2 Attribute Subset Selection 75 2.5.3 Dimensionality Reduction 77 2.5.4 Numerosity Reduction 80 2.6 Data Discretization and Concept Hierarchy Generation 86

  Selection, Integration

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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The Relationship Between Precision-Recall and ROC Curves

www.biostat.wisc.edu

ROC curves, but discussing them is beyond the scope of this paper. Precision-Recall (PR) curves, often used in Informa-tion Retrieval (Manning & Schutze, 1999; Raghavan et al., 1989), have been cited as an alternative to ROC curves for tasks with a large skew in the class dis-

  Curves, Roc curve

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ROC curves, Curves