An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
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
Association rule and quantitative association rule mining among infrequent items
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
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
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
DFP-Growth: an efficient algorithm for mining frequent patterns in dynamic database
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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A research in mining least association rules is still outstanding and thus requiring more attentions. Until now; only few algorithms and techniques are developed to mine the significant least association rules. In addition, mining such rules always suffered from the high computational costs, complicated and required dedicated measurement. Therefore, this paper proposed a scalable model called Critical Least Association Rule (CLAR) to discover the significant and critical least association rules. Experiments with a real and UCI datasets show that the CLAR can generate the critical least association rules, up to 1.5 times faster and less 100% complexity than benchmarked FP-Growth.