Parametric Algorithms for Mining Share Frequent Itemsets
Journal of Intelligent Information Systems
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
Share Based Measures for Itemsets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
Efficient calendar based temporal association rule
ACM SIGMOD Record
Data Mining and Knowledge Discovery
Novel measurement for mining effective association rules
Knowledge-Based Systems
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
CP-tree: a tree structure for single-pass frequent pattern mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
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
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Share-frequent pattern mining discovers more useful and realistic knowledge from database compared to the traditional frequent pattern mining by considering the non-binary frequency values of items in transactions. Therefore, recently share-frequent pattern mining problem becomes a very important research issue in data mining and knowledge discovery. Existing algorithms of share-frequent pattern mining are based on the level-wise candidate set generation-and-test methodology. As a result, they need several database scans and generate-and-test a huge number of candidate patterns. Moreover, their numbers of database scans are dependent on the maximum length of the candidate patterns. In this paper, we propose a novel tree structure ShrFP-Tree (Share-frequent pattern tree) for share-frequent pattern mining. It exploits a pattern growth mining approach to avoid the level-wise candidate set generation-and-test problem and huge number of candidate generation. Its number of database scans is totally independent of the maximum length of the candidate patterns. It needs maximum three database scans to calculate the complete set of share-frequent patterns. Extensive performance analyses show that our approach is very efficient for share-frequent pattern mining and it outperforms the existing most efficient algorithms.