Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
ACM Transactions on Database Systems (TODS)
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Discovering interesting holes in data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Maximum entropy models and subjective interestingness: an application to tiles in binary databases
Data Mining and Knowledge Discovery
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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In the paper a new data mining algorithm for finding the most interesting dependence rules is described. Dependence rules are derived from the itemsets with support significantly different from its expected value and therefore considered interesting. Since such itemsets are distributed non-monotonically in the lattice of all itemsets the support monotonicity property cannot be used for their search. Instead we estimate upper/lower bounds for the support to find itemsets with large interval of possible support values called support quota. Since the support quota is known to be monotonically decreasing the search space can be effectively restricted. Strongly dependent itemsets are selected by computing their expected support using iterative proportional fitting algorithm and comparing it with the real itemset support.