Mining probabilistic generalized frequent itemsets in uncertain databases

8 years 11 months ago

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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 exating 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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications—Data mining; G.3 [Probability and Statistics]: Distribution functions Keywords Probabilistic generalized frequent itemsets, existential probability of generalized itemsets, uncertain databases

Erich Allen Peterson, Peiyi Tang

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