@inproceedings{37d55dca29c14600a5ec561fabbaf41f,
title = "Interestingness of frequent itemsets using Bayesian networks as background knowledge",
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 difference between its support estimated from data and from the Bayesian network. Efficient algorithms are presented for rinding 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.",
keywords = "Association rule, Background knowledge, Bayesian network, Frequent itemset, Interestingness",
author = "Szymon Jaroszewicz and Simovici, \{Dan A.\}",
year = "2004",
doi = "10.1145/1014052.1014074",
language = "English",
isbn = "1581138881",
series = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery (ACM)",
pages = "178--186",
booktitle = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
note = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Conference date: 22-08-2004 Through 25-08-2004",
}