Full Citation

Title: Mining Negative Rules Using GRD

Citation Type: Conference Paper

Publication Year: 2004

ISBN:

ISSN:

DOI: 10.1007/978-3-540-24775-3_20

NSFID:

PMCID:

PMID:

Abstract: GRD is an algorithm for k-most interesting rule discovery. In contrast to association rule discovery, GRD does not require the use of a minimum support constraint. Rather, the user must specify a measure of interestingness and the number of rules sought (k). This paper reports efficient techniques to extend GRD to support mining of negative rules. We demonstrate that the new approach provides tractable discovery of both negative and positive rules.

Url: http://link.springer.com/10.1007/978-3-540-24775-3_20

User Submitted?: No

Authors: Thiruvady, Dhananjay R.; Webb, Geoff I.

Conference Name: Pacific-Asia Conference on Knowledge Discovery and Data Mining

Publisher Location: Sydney, Australia

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States