A Classification Algorithm Based on Association Rule Mining

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.

I. Introduction

The classification rules mining and association rules mining are two important areas of data mining [1]. The classic associative classification algorithm based on class association rules named CBA [2]which integrated the above two important mining technologies was presented by Bing Liu of National University of Singapore in the knowledge discovery in databases (KDD) International Conference held in New York, 1998. Since then the prelude of the associative classification was opened. Good classification accuracy of associative classification algorithm has been confirmed in the past ten years through a number of studies and experiments.