Full Citation

Title: Beyond Association Rules: Generalized Rule Discovery

Citation Type: Miscellaneous

Publication Year: 2003

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Abstract: Generalized rule discovery is a rule discovery framework that subsumes association rule discovery and the type of search employed to find individual rules in classification rule discovery. This new rule discovery framework escapes the limitations of the support-confidence framework inherent in association rule discovery. This empowers data miners to identify the types of rules that they wish to discover and develop efficient algorithms for discovering those rules. This paper presents a scalable algorithm applicable to a wide range of generalized rule discovery task and demonstrates its efficiency.

Url: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.15.633&rep=rep1&type=pdf

User Submitted?: No

Authors: Webb, Geoffrey I; Zhang, Songmao

Publisher: Monash University

Data Collections: IPUMS USA

Topics: Other

Countries: United States