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KDD
2009
ACM

Probabilistic frequent itemset mining in uncertain databases

14 years 3 months ago
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...
Andreas Züfle, Florian Verhein, Hans-Peter Kr
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors Andreas Züfle, Florian Verhein, Hans-Peter Kriegel, Matthias Renz, Thomas Bernecker
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