Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Interesting Associations without Support Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Rare association rule mining via transaction clustering
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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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 significance. 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 confirm that our approach outperforms both the Apriori-Inverse(Koh et al. 2006) and Relative Support (Yun et al. 2003) algorithms.