A Novel Rule Ordering Approach in Classification Association Rule Mining

摘要

A Classification Association Rule (CAR), a common type of mined knowledge in Data Mining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an "antecedent => consequent-class" rule. Classification Association Rule Mining (CARM) is a recent Classification Rule Mining (CRM) approach that builds an Association Rule Mining (ARM) based classifier using CARs. Regardless of which particular methodology is used to build it, a classifier is usually presented as an ordered CAR list, based on an applied rule ordering strategy. Five existing rule ordering mechanisms can be identified: (1) Confi-dence-Support-size_of_Antecedent (CSA), (2) size_of_Antecedent-Confidence-Support (ACS), (3) Weighted Relative Accuracy (WRA), (4) Laplace Accuracy, and (5) χ~2 Testing. In this paper, we divide the above mechanisms into two groups: (ⅰ) pure "support-confidence" framework like, and (ⅱ) additive score assigning like. We consequently propose a hybrid rule ordering approach by combining one approach taken from (ⅰ) and another approach taken from (ⅱ). The experimental results show that the proposed rule ordering approach performs well with respect to the accuracy of classification.