Data Mining: Concepts and Techniques
4.3.5 Class Description: Presentation of Both Characterization and Comparison 215 4.4 Summary 218 Exercises 219 Bibliographic Notes 223 Chapter 5 Mining Frequent Patterns, Associations, and Correlations 227 5.1 Basic Concepts and a Road Map 227 5.1.1 Market Basket Analysis: A Motivating Example 228
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Data Mining: Concepts and Techniques
hanj.cs.illinois.edu2.4 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
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:
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