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Frequent itemset mining in uncertain transaction databases semantically andcomputationally differs from traditional techniques applied on standard(certain) transaction databases. Uncertain transaction databases consist ofsets of existentially uncertain items. The uncertainty of items in transactionsmakes traditional techniques inapplicable. In this paper, we tackle the problemof finding probabilistic frequent itemsets based on possible world semantics.In this context, an itemset X is called frequent if the probability that Xoccurs in at least minSup transactions is above a given threshold. We make thefollowing contributions: We propose the first probabilistic FP-Growth algorithm(ProFP-Growth) and associated probabilistic FP-Tree (ProFP-Tree), which we useto mine all probabilistic frequent itemsets in uncertain transaction databaseswithout candidate generation. In addition, we propose an efficient technique tocompute the support probability distribution of an itemset in linear time usingthe concept of generating functions. An extensive experimental sectionevaluates the our proposed techniques and shows that our ProFP-Growth approachis significantly faster than the current state-of-the-art algorithm.

Probabilistic Frequent Pattern Growth for Itemset Mining in Uncertain Databases (Technical Report)