By Topic

Adaptive fuzzy clustering based on Genetic 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

3 Author(s)
Zhu Lianjiang ; Coll. of Inf. Sci. & Eng., Univ. of Jinan, Jinan, China ; Qu Shouning ; Du Tao

Traditional Fuzzy c-means (FCM) algorithm is commonly used in unsupervised learning. However, there are some limitations. Cluster number should be determined and the cluster center should be initialized before classification. A new algorithm is proposed in the paper. The best cluster number is obtained by analyzing cluster validity function and the cluster center is initialized by HCM. The data set is classified with Fuzzy c-means algorithm based on Genetic algorithm. The experimental results indicate the effectiveness and adaptability of the new algorithm.

Published in:

Advanced Computer Control (ICACC), 2010 2nd International Conference on  (Volume:5 )

Date of Conference:

27-29 March 2010