Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
Parametric Algorithms for Mining Share Frequent Itemsets
Journal of Intelligent Information Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Share Based Measures for Itemsets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Efficient algorithms for incremental utility mining
Proceedings of the 2nd international conference on Ubiquitous information management and communication
HHUIF and MSICF: Novel algorithms for privacy preserving utility mining
Expert Systems with Applications: An International Journal
Two-phase algorithms for a novel utility-frequent mining model
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Effective utility mining with the measure of average utility
Expert Systems with Applications: An International Journal
Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
An incremental mining algorithm for high utility itemsets
Expert Systems with Applications: An International Journal
Mining high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
ShrFP-tree: an efficient tree structure for mining share-frequent patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
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Itemset share has been proposed as a measure of the importance of itemsets for mining association rules. The value of the itemset share can provide useful information such as total profit or total customer purchased quantity associated with an itemset in database. The discovery of share-frequent itemsets does not have the downward closure property. Existing algorithms for discovering share-frequent itemsets are inefficient or do not find all share-frequent itemsets. Therefore, this study proposes a novel Fast Share Measure (FSM) algorithm to efficiently generate all share-frequent itemsets. Instead of the downward closure property, FSM satisfies the level closure property. Simulation results reveal that the performance of the FSM algorithm is superior to the ZSP algorithm two to three orders of magnitude between 0.2% and 2% minimum share thresholds.