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Generalized Closed Itemsets for Association Rule Mining

Pudi, Vikram and Haritsa, Jayant R (2003) Generalized Closed Itemsets for Association Rule Mining. In: 19th International Conference on Data Engineering (ICDE’03), 5-8 March, 2003, Bangalore, India, pp. 714-716.

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Abstract

The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning "uninteresting" rules following the observation that most rules can be derived from other rules. In this paper, we propose a new framework, namely, the generalized closed (or g-closed) itemset framework. By allowing for a small tolerance in the accuracy of itemset supports, we show that the number of such redundant rules is far more than what was previously estimated. Our scheme can be integrated into both levelwise algorithms (Apriori) and two-pass algorithms (ARMOR). We evaluate its performance by measuring the reduction in output size as well as in response time. Our experiments show that incorporating gclosed itemsets provides significant performance improvements on a variety of databases.

Item Type: Conference Poster
Additional Information: ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Information Sciences > Supercomputer Education & Research Centre
Date Deposited: 15 Jul 2004
Last Modified: 19 Sep 2010 04:14
URI: http://eprints.iisc.ernet.in/id/eprint/1047

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