By Topic

A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data

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)
Rasouli, A. ; Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai, Malaysia ; Bin Maarof, M.A. ; Shamsi, M.

Clustering is the process of gathering objects into groups based on their feature's similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is higher than other famous clustering algorithm and ensures that the WKM is a good solution for real world problems.

Published in:

Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of

Date of Conference:

4-7 Dec. 2009