By Topic

Weighted fuzzy clustering on subsets of variables

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)
Sato-Ilic, M. ; Fac. of Syst. & Inf. Eng., Tsukuba Univ., Tsukuba

A fuzzy clustering method considering weights of variable-based dissimilarities over objects in the subspace of the objectpsilas space is proposed. In order to estimate the weights, we propose two methods. One is a method in which a conventional fuzzy clustering method is directly used for the variable-based dissimilarity data. The other is to use a new objective function. Exploiting the weights, we define a dissimilarity assigned with the significance of each variable for the classification and reduce the number of variables. We can implement a fuzzy clustering method under the intrinsically weighted classification structure on the subspace of data. Several numerical examples show the improved performance and the applicability of our proposed method.

Published in:

Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on

Date of Conference:

12-15 Feb. 2007