Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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)
Pfp: parallel fp-growth for query recommendation
Proceedings of the 2008 ACM conference on Recommender systems
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
Scalable model for mining critical least association rules
ICICA'10 Proceedings of the First international conference on Information computing and applications
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
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
WLAR-Viz: weighted least association rules visualization
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
Tracing significant association rules using critical least association rules model
International Journal of Innovative Computing and Applications
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Development of least association rules mining algorithms are very challenging in data mining. The complexity and excessive in computational cost are always become the main obstacles as compared to mining the frequent rules. Indeed, most of the previous studies still adopting the Apriori-like algorithms which are very time consuming. To address this issue, this paper proposes a scalable trie-based algorithm named SLP-Growth. This algorithm generates the significant patterns using interval support and determines its correlation. Experiments with the real datasets show that the SLP-algorithm can discover highly positive correlated and significant of least association. Indeed, it also outperforms the fast FP-Growth algorithm up to two times, thus verifying its efficiency.