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


Download (1314Kb)


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.

Statistics for USQ ePrint 2087
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Copyright 2006 IEEE. Personal use of this material is permitted. This material is posted here with permission of the IEEE. 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.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
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 : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: 10.1109/TKDE.2006.1599385
URI: http://eprints.usq.edu.au/id/eprint/2087

Actions (login required)

View Item Archive Repository Staff Only