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Efficient Mining of Indirect Associations Using HI-Mine

Summary: Efficient Mining of Indirect Associations
Using HI-Mine
Qian Wan and Aijun An
Department of Computer Science, York University
Toronto, Ontario M3J 1P3 Canada
{qwan, aan}@cs.yorku.ca
Abstract. Discovering association rules is one of the important tasks in data
mining. While most of the existing algorithms are developed for efficient
mining of frequent patterns, it has been noted recently that some of the
infrequent patterns, such as indirect associations, provide useful insight into the
data. In this paper, we propose an efficient algorithm, called HI-mine, based on
a new data structure, called HI-struct, for mining the complete set of indirect
associations between items. Our experimental results show that HI-mine's
performance is significantly better than that of the previously developed
algorithm for mining indirect associations on both synthetic and real world data
sets over practical ranges of support specifications.
1 Introduction
Since it was first introduced by Agrawal et al. [4] in 1993, association rule mining has
been studied extensively by many researchers. As a result, many algorithms have
been proposed to improve the running time for generating association rules and


Source: An, Aijun - Department of Computer Science, York University (Toronto)


Collections: Computer Technologies and Information Sciences