Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 0.00 |
Most algorithms for frequent pattern mining use a support-based pruning strategy to prune a combinatorial search space. However, they are not effective for finding correlated patterns with similar levels of support. In additional, traditional patterns mining algorithms rarely consider weighted pattern mining. In this paper, we present a new algorithm, WHFPMiner(Weighted Highly-correlated Frequent Patterns Miner) in which a new objective measure, called weighted h-confidence, is developed to mine weighted highly-correlated frequent patterns with similar levels of weighted support. Adopting an improved weighted FP-tree structure, this algorithm exploits both cross-weighted support and anti-monotone properties of the weighted h-confidence measure for the efficient discovery of weighted hyperclique patterns. A comprehensive performance study shows that WHFPMineris efficient and fast for finding weighted highly-correlated frequent patterns. Moreover, it generates fewer but more valuable patterns with the high correlation.