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 . 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...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Andreas Züfle, Florian Verhein, Hans-Peter Kriegel, Matthias Renz, Thomas Bernecker
• Keywords: Data Mining, Frequent Itemset Mining, KDD 2009, Probabilistic Frequent Itemset, Probabilistic Frequent Itemsets