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 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

• Type: Journal
• Year: 2013
• Where: ACMSE
• Authors: Erich Allen Peterson, Peiyi Tang
• Keywords: ACMSE 2013, Theoretical Computer Science