Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A New Method for Finding Generalized Frequent Itemsets in Generalized Association Rule Mining
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
Probabilistic frequent itemset mining in uncertain databases
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating probabilistic frequent itemset mining: a model-based approach
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Researchers have recently defined and presented the theoretical concepts and an algorithm necessary for mining so-called probabilistic frequent itemsets in uncertain databases---based on possible world semantics. Further, there exist algorithms for mining so-called generalized itemsets in certain databases, where a taxonomy exists relating concrete items to abstract (generalized) items not in the database. Currently, no research has been done in formulating a theory and algorithm for mining generalized itemsets from uncertain databases. Using probability theory and possible world semantics, we formulate a method for calculating the probability a generalized item will occur within an uncertain transaction.