CPAR: Classification based on Predictive Association Rules

Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as [7, 6], achieves higher classification accuracy than traditional classification approaches such as C4.5. However, the approach also suffers from two major deficiencies: (1) it generates a very large number of association rules, which leads to high processing overhead; and (2) its confidence-based rule evaluation measure may lead to overfitting. In comparison with associative classification, traditional rule-based classifiers, such as C4.5, FOIL and RIPPER, are substantially faster but their accuracy, in most cases, may not be as high. In this paper, we…