On optimal rule discovery

Li, Jiuyong (2006) On optimal rule discovery. IEEE Transactions on Knowledge and Data Engineering, 18 (4). pp. 460-471. ISSN 1041-4347

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Abstract

In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here we present a unified framework for the discovery of a family of optimal rule sets, and characterise the relationships with other rule discovery schemes such as non-redundant association rule discovery. We theoretically and empirically show that optimal rule discovery is significantly more efficient than the association rule discovery independent of data structure and implementation. Optimal rule discovery is an efficient alternative to association rule discovery, especially when the minimum support is low.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 11 Oct 2007 00:56
Last Modified: 02 Jul 2013 22:42
Uncontrolled Keywords: Data mining, rule discovery, optimal rule set
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: https://doi.org/10.1109/TKDE.2006.1599385
URI: http://eprints.usq.edu.au/id/eprint/2087

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