Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining association rules with adjustable accuracy
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
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
ShrFP-tree: an efficient tree structure for mining share-frequent patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Itemset share, the fraction of some numerical total contributed by items when they occur in itemsets, has been proposed as a measure of the importance of itemsets in association rule mining. The IAB and CAC algorithms are able to find share frequent itemsets that have infrequent subsets. These algorithms perform well, but they do not always find all possible share frequent itemsets. In this paper, we describe the incorporation of a threshold factor into these algorithms. The threshold factor can be used to increase the number of frequent itemsets found at a cost of an increase in the number of infrequent itemsets examined. The modified algorithms are tested on a large commercial database. Their behavior is examined using principles of classifier evaluation from machine learning.