A compress-based association mining algorithm for large dataset

Abstract

The association mining is one of the primary sub-areas in the field of data mining. This technique had been used in numerous practical applications, including consumer market basket analysis, inferring patterns from web page access logs, network intrusion detection and pattern discovery in biological applications. Most of the traditional association-mining algorithms assume that whole dataset can be loaded in the main memory. Hence, problem arise when such algorithms is applied in large dataset. In this paper we present a new algorithm for association mining. Our algorithm is efficient when the size of dataset is huge that cannot be load in the main memory. The proposed algorithm also reduces the frequent itemsets search space, by eliminating non-frequent 1-itemsets after the first pass. Our performance evaluation shows algorithm out-performs Apriori algorithm in different datasets. © Springer-Verlag Berlin Heidelberg 2003.