On optimal rule discovery

Article

Li, Jiuyong. 2006. "On optimal rule discovery." IEEE Transactions on Knowledge and Data Engineering. 18 (4), pp. 460-471. https://doi.org/10.1109/TKDE.2006.1599385
Article Title

On optimal rule discovery

ERA Journal ID17876
Article CategoryArticle
Authors
AuthorLi, Jiuyong
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Journal Citation18 (4), pp. 460-471
Number of Pages12
Year2006
PublisherIEEE (Institute of Electrical and Electronics Engineers)
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2006.1599385
Web Address (URL)https://ieeexplore.ieee.org/document/1599385
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.

KeywordsData mining, rule discovery, optimal rule set
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
Public Notes

© 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.

Byline AffiliationsDepartment of Mathematics and Computing
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