Performance Comparison of Rule Based Classification Algorithms

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Provided by: Interscience Open Access Journals
Topic: Data Management
Format: PDF
Classification based on Predictive Association Rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. CPAR inherits the basic ideas of FOIL (First Order Inductive Learner) algorithm and PRM (Predictive Rule Mining) algorithm in rule generation.
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