Different fuzzy cluster validity indexes for the evaluation of the quality of the resulting partitioning
by Paola Perchinunno; Silvestro Montrone
International Journal of Innovative Computing and Applications (IJICA), Vol. 7, No. 2, 2016

Abstract: Fuzzy clustering is useful to mine complex and multi-dimensional datasets, where the members have partial or fuzzy relations. The procedure of evaluating the results of a fuzzy clustering algorithm is known under the term cluster validity. There are three principal approaches to investigate cluster validity: external, internal and relative criteria (Theodoridis and Koutroubas, 1999). These methods give an indication of the quality of the resulting partitioning and, so, they can be considered as an instrument available to experts in order to assess the results of fuzzy clustering. In this work we apply some indexes to evaluate the quality of the results obtained from a case study.

Online publication date: Wed, 06-Jul-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Innovative Computing and Applications (IJICA):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com