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Abstract: The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the absolute di#erence between its support estimated from data and from the Bayesian network. E#cient algorithms are presented for finding interestingness of a collection of frequent itemsets, and for finding all attribute sets with a given minimum interestingness. Practical usefulness of the algorithms and their e#ciency have ... (Update)
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BibTeX entry: (Update)
@misc{ jaroszewicz-interestingness,
author = "Szymon Jaroszewicz and Dan A. Simovici",
title = "Interestingness of Frequent Itemsets Using Bayesian Networks as Background
Knowledge",
url = "citeseer.ist.psu.edu/jaroszewicz04interestingness.html" }
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4 A general measure of rule interestingness - Jaroszewicz, Simovici - 2001
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Documents on the same site (http://www.cs.umb.edu/~sj/biblio/biblio.html): More
Pruning Redundant Association Rules Using Maximum Entropy.. - Jaroszewicz, Simovici (2002) (Correct)
A General Measure of Rule Interestingness - Jaroszewicz, Simovici (2002) (Correct)
A General Measure of Rule Interestingness - Jaroszewicz, Simovici (2001) (Correct)
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