A Novel Rule Ordering Approach in Classification Association Rule Mining

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) Confidence-Support-size_of_Antecedent (CSA), (2) size_of_Antecedent-ConfidenceSupport (ACS), (3) Weighted Relative Accuracy (WRA), (4) Laplace Accuracy, and (5) χ2 Testing. In this paper, we divide the above mechanisms into two groups: (i) pure “support-confidence” framework like, and (ii) additive ...

• Type: Conference
• Year: 2007
• Where: MLDM
• Authors: Yanbo J. Wang, Qin Xin, Frans Coenen
• Keywords: Association Rule Mining, Classification Association Rule, Machine Learning, MLDM 2007, Rule Mining