By Topic

Variation approaches to feature-weight selection and application to fuzzy clustering

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

3 Author(s)
Wen-Liang Hung ; Grad. Inst. of Comput. Sci., Nat. Hsinchu Univ. of Educ., Hsinchu ; Miin-Shen Yang ; De-Hua Chen

In statistics field, variation plays an important role. This is because greater variations in some features of data can provide more important information. Therefore, in this paper, we use this idea to select feature-weights in data. The proposed approach is simple to compute and interpret for feature-weights selection. Compared with the feature-weights proposed by Wang et al., Modha and Spangler, Pal et al. & Basak et al., we find that the proposed method provides a better clustering performance for the Iris data and color image segmentation and also has lower computational complexity..

Published in:

Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on

Date of Conference:

1-6 June 2008