Abstract—In the recent years, there has been significant
development in the field of Probabilistic Frequent Itemset
Mining (PFIM). Despite the complexity of calculating the
frequentness probability of an itemset, approximation
techniques allow us to reduce the complexity of the problem
with very low approximation error. In this paper we investigate
how to incorporate hierarchical taxonomies into the attribute
uncertainty model, which assumes independence between the
existential probability of items in a transaction. We propose
scalable methods which can reduce noise, and ensure
consistency of the transactions by approximating the
dependencies between attributes implied by a background
hierarchical taxonomy. We also perform experiments in order
to evaluate the scalability, accuracy of the approximation, as
well as the denoising performance of the proposed methods.
Index Terms—Probabilistic frequent itemset mining,
generalized rules, hierarchical background knowledge.
André Melo and Johanna Völker are with University of Mannheim,
Germany (e-mail: andre@informatik.uni-mannheim-de,
johanna@informatik.uni-mannheim-de).
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Cite: André Melo and Johanna Völker, "Probabilistic Frequent Itemset Mining with Hierarchical Background Knowledge," International Journal of Knowledge Engineering vol. 1, no. 2, pp. 92-99, 2015.
