By Topic

FRBC: A Fuzzy Rule-Based Clustering Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Mansoori, E.G. ; Sch. of Electr. & Comput. Eng., Shiraz Univ., Shiraz, Iran

Fuzzy clustering is superior to crisp clustering when the boundaries among the clusters are vague and ambiguous. However, the main limitation of both fuzzy and crisp clustering algorithms is their sensitivity to the number of potential clusters and/or their initial positions. Moreover, the comprehensibility of obtained clusters is not expertized, whereupon in data-mining applications, the discovered knowledge is not understandable for human users. To overcome these restrictions, a novel fuzzy rule-based clustering algorithm (FRBC) is proposed in this paper. Like fuzzy rule-based classifiers, the FRBC employs a supervised classification approach to do the unsupervised cluster analysis. It tries to automatically explore the potential clusters in the data patterns and identify them with some interpretable fuzzy rules. Simultaneous classification of data patterns with these fuzzy rules can reveal the actual boundaries of the clusters. To illustrate the capability of FRBC to explore the clusters in data, the experimental results on some benchmark datasets are obtained and compared with other fuzzy clustering algorithms. The clusters specified by fuzzy rules are human understandable with acceptable accuracy.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:19 ,  Issue: 5 )