document: Probabilistic frequent itemset mining in uncertain databases Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard `` certain '' transaction databases . The consideration of existential uncertainty of item ( sets ) , indicating the probability that an item ( set ) occurs in a transaction , makes traditional techniques inapplicable . In this paper , we introduce new probabilistic formulations of frequent itemsets based on possible world semantics . In this probabilistic context , an itemset X is called frequent if the probability that X occurs in at least minSup transactions is above a given threshold τ . To the best of our knowledge , this is the first approach addressing this problem under possible worlds semantics . In consideration of the probabilistic formulations , we present a framework which is able to solve the Probabilistic Frequent Itemset Mining ( PFIM ) problem efficiently . An extensive experimental evaluation investigates the impact of our proposed techniques and shows that our approach is orders of magnitude faster than straight-forward approaches .
doc_bio_tags: O B I I O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O