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General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2015 Vol.1(2): 92-99 ISSN: 2382-6185
DOI: 10.7763/IJKE.2015.V1.16

Probabilistic Frequent Itemset Mining with Hierarchical Background Knowledge

André Melo and Johanna Völker
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).


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.

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