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Mining dependence rules by finding largest itemset support quota

: Savinov, A.


Association for Computing Machinery -ACM-, Special Interest Group on Applied Computing -SIGAPP-:
SAC 2004. Proceedings of the 2004 ACM Symposium on Applied Computing : Nicosia, Cyprus, March 14-17, 2004
New York: ACM Press, 2004
ISBN: 1-581-13812-1
Symposium on Applied Computing (SAC) <19, 2004, Nicosia>
Conference Paper
Fraunhofer AIS ( IAIS) ()
data mining; dependence rules; support bounding; support quota; expected support

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.