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Type: Conference paper
Title: Mining the smallest association rule set for predictions
Author: Li, J.
Shen, H.
Topor, R.
Citation: Proceedings : 2001 IEEE International Conference on Data Mining, 29 November--2 December 2001, San Jose, California / edited by Nick Cercone, T.Y. Lin, Xindong Wu (eds.), pp. 361-368
Publisher: IEEE Computer Society
Issue Date: 2001
ISBN: 0769511198
ISSN: 1550-4786
Conference Name: (29 Nov 2001 : San Jose, CA, USA)
Abstract: Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We present an algorithm to directly generate the informative rule set, i.e., without generating all frequent itemsets first, and that accesses the database less often than other unconstrained direct methods. We show experimentally that the informative rule set is much smaller than both the association rule set and the non-redundant association rule set, and that it can be generated more efficiently.
Description: ©2001 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.
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Computer Science publications

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