A novel associative classification algorithm: A combination of LAC and CMAR with new measure of weighted effect of each rule group
Pei-Yi Hao, Yu-De Chen · 2011
In recent, Association Classification not only has widely adopted but also has performed well in data mining. The literatures have been argued that the small disjunction and using multiple class-association rules have significant effect on classification accuracy. This paper is based on CMAR (Classification based on Multiple Class-Association Rules) and Adriano Veloso proposed Lazy Associative Classifier algorithm for Small Disjunction mining. In addition, we collocate with a new weight calculation method in our algorithm to solve weight bias problem of CMAR. This paper uses UCI 26 data set for experiment on our proposed algorithm. The finally results convincingly demonstrated that our proposed algorithm is high accuracy.