Mining probabilistic generalized frequent itemsets in uncertain databases

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.