Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Mining dependence rules by finding largest itemset support quota
 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 1417, 2004 New York: ACM Press, 2004 ISBN: 1581138121 pp.525529 
 Symposium on Applied Computing (SAC) <19, 2004, Nicosia> 

 English 
 Conference Paper 
 Fraunhofer AIS ( IAIS) () 
 data mining; dependence rules; support bounding; support quota; expected support 
Abstract
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 nonmonotonically 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.