Interestingness of frequent itemsets using Bayesian networks as background knowledge

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 difference between its support estimated from data and from the Bayesian network. Efficient 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 efficiency have been verified experimentally. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-data mining General Terms Algorithms Keywords association rule, frequent itemset, background knowledge, interestingness, Bayesian network

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Szymon Jaroszewicz, Dan A. Simovici
• Keywords: Bayesian Network, Data Mining, Frequent Itemsets, KDD 2004, Terms Algorithms Keywords