Closure based integrated approach for associative classifier

Abstract

Building a classifier using association rules for classification task is a supervised data mining technique called Associative Classification (AC). Experiments show that AC has higher degree of classification accuracy than traditional approaches. The learning methodology used in most of the AC algorithms is apriori based. Thus, these algorithms inherit some of the Apriori’s deficiencies like multiple scans of dataset and accumulative increase of number of rules. Closed itemset based approach is a solution to the above mentioned drawbacks. Here, we proposed a closed itemset based associative classifier (ACFIST) to generate the class association rules (CARs) along with biclusters. In this paper, we have also focused on generating lossless and condensed set of rules as it is based on closed concept. Experiments done on benchmark datasets to show the amount of result it is generating.