Abstract
Rare itemsets are likely to be of great interest because they often relate to high-impact transactions which may give rise to rules of great practical signi cance. Research into the rare association rule mining problem has gained momentum in the recent past. In this paper, we propose a novel approach that captures such rare rules while ensuring that redundant rules are eliminated. Extensive testing on real-world datasets from the UCI repository con rm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms.
Keywords
Rare Association Rule Mining; Apriori-Inverse; Non-Redundant Itemset
Date
2009
Source
2009 Australasian Data Mining Conference , Melbourne, Australia, published in: Proceeding of the 2009 Australasian Data Mining Conference, vol.101, pp.69 - 74 (6)
Item Type
Conference Contribution
Publisher
Australian Computer Society (ACS)
Rights Statement
Copyright © 2009, Australian Computer Society, Inc. This paper appeared at the Eighth Australasian Data Mining Conference (AusDM 2009), Melbourne, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 101, Paul J. Kennedy, Kok-Leong Ong and Peter Christen, Ed. Reproduction for academic, not-for pro t purposes permitted provided this text is included.