Session:

Knowledge Representation, Decision Support and Expert Systems
Tuesday, March 02, 2004, 10.50 – 11.10

Session Chair:
Vice Chair:

A. Dobnikar
M. Savoji

Paper Title:

An Analysis of Association Rule Mining Algorithms

Author(s):

R. Iváncsy, Budapest University of Technology and Economics, Hungary
I. Vajk, Budapest University of Technology and Economics, Hungary

Abstract:

The association rule mining is a fundamentally important task in the process of knowledge discovery in large databases. Several algorithms have been developed for single-level, single-dimensional, Boolean association rule mining. Some of them require a small amount of memory, but heavy disk access (such as Apriori-like algorithms); others necessitate low I/O activity, but large amount of memory (such as FP-growth). Different algorithms support different applications and requirements depending on the technical background. For this reason it is desirable to classify these algorithms. In this paper a trade-off is illustrated, namely, which aspects of selection should be considered, when one classifies association rule mining algorithms. Well known algorithms are categorized with these criteria, and the concept of restricted association rule mining is introduced. Necessary modifications are also shown to the algorithms assuming that not all frequent itemsets are needed, only those with maximal size of a given threshold. The paper examines the mining time for both the original and the modified algorithms, and calculates the profit.