Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Jumping emerging patterns with negation in transaction databases - Classification and discovery
Information Sciences: an International Journal
Transactions on rough sets XII
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In Data Mining applications of the frequent sets problem, such as finding association rules, a commonly used generalization is to see each transaction as the characteristic function of the corresponding itemset. This allows one to find also correlations between items not being in the transactions; but this may lead to the risk of a large and hard to interpret output. We propose a bottom-up algorithm in which the exploration of facts corresponding to items not being in the transactions is delayed with respect to positive information of items being in the transactions. This allows the user to dose the association rules found in terms of the amount of correlation allowed between absences of items. The algorithm takes advantage of the relationships between the corresponding frequencies of such itemsets. With a slight modification, our algorithm can be used as well to find all frequent itemsets consisting of an arbitrary number of present positive attributes and at most a predetermined number k of present negative attributes.