Welcome to the InfoSci Platform
Reference Hub10
Finding Associations in Composite Data Sets: The CFARM Algorithm

Finding Associations in Composite Data Sets: The CFARM Algorithm

M. Sulaiman Khan, Maybin Muyeba, Frans Coenen, David Reid, Hissam Tawfik
Copyright: © 2011 |Volume: 7 |Issue: 3 |Pages: 29
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781613506356|DOI: 10.4018/jdwm.2011070101
Cite Article Cite Article

MLA

Khan, M. Sulaiman, et al. "Finding Associations in Composite Data Sets: The CFARM Algorithm." IJDWM vol.7, no.3 2011: pp.1-29. http://doi.org/10.4018/jdwm.2011070101

APA

Khan, M. S., Muyeba, M., Coenen, F., Reid, D., & Tawfik, H. (2011). Finding Associations in Composite Data Sets: The CFARM Algorithm. International Journal of Data Warehousing and Mining (IJDWM), 7(3), 1-29. http://doi.org/10.4018/jdwm.2011070101

Chicago

Khan, M. Sulaiman, et al. "Finding Associations in Composite Data Sets: The CFARM Algorithm," International Journal of Data Warehousing and Mining (IJDWM) 7, no.3: 1-29. http://doi.org/10.4018/jdwm.2011070101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In this paper, a composite fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.