An efficient compression technique for frequent itemset generation in association rule mining

Authors:
Mafruz Zaman Ashrafi;David Taniar;Kate Smith
Affiliations:
School of Business Systems, Monash University, Clayton, VIC, Australia;School of Business Systems, Monash University, Clayton, VIC, Australia;School of Business Systems, Monash University, Clayton, VIC, Australia
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

Association Rule mining is one of the widely used data mining techniques. To achieve a better performance, many efficient algorithms have been proposed. Despite these efforts, we are often unable to complete a mining task because these algorithms require a large amount of main memory to enumerate all frequent itemsets, especially when dataset is large or the user-specified support is low. Thus, it becomes apparent that we need to have an efficient main memory handling technique, which allows association rule mining algorithms to handle larger datasets in main memory. To achieve this goal, in this paper we propose an algorithm for vertical association rule mining that compresses a vertical dataset in an efficient manner, using bit vectors. Our performance evaluations show that the compression ratio attained by our proposed technique is better than those of the other well known techniques.