By Topic

Comparison of fuzzy clustering algorithms for classification

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

2 Author(s)
Almeida, R.J. ; Dept. of Mech. Eng., Tech. Univ. of Lisbon ; Sousa, J.M.C.

The identification of fuzzy models for classification is a very complex task. Often, real world databases have a large number of features and the most relevant ones must be chosen. Recently, a new automatic feature selection for classification problems was proposed to construct compact fuzzy classification models. This technique used the classical fuzzy c-means algorithm. However, other fuzzy clustering algorithms, such as possibilistic c-means, fuzzy possibilistic c-means or possibilistic fuzzy c-means can be used to cluster the data. An open topic of research is what clustering algorithms can be used to derive fuzzy models for classification. This paper addresses this topic, by comparing fuzzy clustering algorithms in terms of computational efficiency and accuracy in classification problems. The algorithms were tested in well-known data sets: iris plant, wine, hepatitis, breast cancer and in a difficult real-world problem: the prediction of bankruptcy

Published in:

Evolving Fuzzy Systems, 2006 International Symposium on

Date of Conference:

Sept. 2006