document: 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 .
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