Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
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
Parametric Algorithms for Mining Share-Frequent Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
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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The share measure for itemsets provides useful information about numerical values associated with transaction items, that the support measure cannot. Finding share frequent itemsets is difficult because share frequency is not downward closed when it is defined in terms of the itemset as a whole. The Item Add-back and Combine All Counted algorithms do not rely on downward closure and thus, are able to find share frequent itemsets that have infrequent subsets. These heuristic algorithms predict which itemsets should be counted in the current pass using information available at no additional processing cost.