Full Citation

Title: K-Optimal Rule Discovery

Citation Type: Journal Article

Publication Year: 2005

ISBN:

ISSN:

DOI: 10.1007/s10618-005-0255-4

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Abstract: K-optimal rule discovery finds the K rules that optimize a user-specified measure of rule value with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of K-optimal rule discovery tasks and demonstrates its efficiency.

Url: http://link.springer.com/10.1007/s10618-005-0255-4

User Submitted?: No

Authors: Webb, Geoffrey I.; Zhang, Songmao

Periodical (Full): Data Mining and Knowledge Discovery

Issue: 1

Volume: 10

Pages: 39-79

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States