A Classification Algorithm Based on Association Rule Mining

Abstract

The main difference of the associative classification algorithms is how to mine frequent item sets, analyze the rules exported and use for classification. This paper presents an associative classification algorithm based on Trie-tree that named CARPT, which remove the frequent items that cannot generate frequent rules directly by adding the count of class labels. And we compress the storage of database using the two-dimensional array of vertical data format, reduce the number of scanning the database significantly, at the same time, it is convenient to count the support of candidate sets. So, time and space can be saved effectively. The experiment results show that the algorithm is feasible and effective.