Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient association rule mining among infrequent items
Efficient association rule mining among infrequent items
Cluster-based classification using self-organising maps for medical image databases
International Journal of Innovative Computing and Applications
Mining significant least association rules using fast SLP-growth algorithm
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
International Journal of Innovative Computing and Applications
Design of an intelligent novelty detection application
International Journal of Innovative Computing and Applications
Data mining with a parallel rule induction system based on gene expression programming
International Journal of Innovative Computing and Applications
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Mining of least association rules from large databases has received a great attention in knowledge discovery. These rules are very useful especially in tracing the unexceptional events or situations. Until this moment, the ratio of studies in this area is still unbalanced as compared to mine frequent rules. The difficulties level of mining these rules as compared to frequent rules are different since it involves with the excessive in computational costs, rather complicated and entailed a dedicated measurement. Therefore, this paper proposed an efficient model called critical least association rule CLAR to mine the significant rules so called critical least association rules. Several experiments with real and UCI datasets has shown that the CLAR successfully in producing the critical least association rules, up to 1.5 times faster and less 96% complexity than benchmarked FP-growth algorithm.