An adaptive approach to mining frequent itemsets efficiently

Abstract

The mining of frequent itemsets is a fundamental and important task of data mining. To improve the efficiency in mining frequent itemsets, many researchers developed smart data structures to represent the database, and designed divide-and-conquers approaches to generate frequent itemsets from these data structures. However, the features of real databases are diversified and the features of local databases in the mining process may also change. Consequently, different data structures may be utilized in the mining process to enhance efficiency. This study presents an adaptive mechanism to select suitable data structures depending on database densities: the Frequent Pattern List (FPL) for sparse databases, and the Transaction Pattern List (TPL) for dense databases. Experimental results verified the effectiveness of this approach.