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
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Parametric Algorithms for Mining Share Frequent Itemsets
Journal of Intelligent Information Systems
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
Profit Mining: From Patterns to Actions
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
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
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining high average-utility itemsets
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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
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
| Hi-index | 0.00 |
The value of the itemset share is one way of evaluating the magnitude of an itemset. From business perspective, itemset share values reflect more the significance of itemsets for mining association rules in a database. The Share-counted FSM (ShFSM) algorithm is one of the best algorithms which can discover all share-frequent itemsets efficiently. However, ShFSM wastes the computation time on the join and the prune steps of candidate generation in each pass, and generates too many useless candidates. Therefore, this study proposes the Direct Candidates Generation (DCG) algorithm to directly generate candidates without the prune and the join steps in each pass. Moreover, the number of candidates generated by DCG is less than that by ShFSM. Experimental results reveal that the proposed method performs significantly better than ShFSM.