By Topic

A Fast and Effective Kernel-Based K-Means Clustering 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

2 Author(s)
Kong Dexi ; Jinan Univ., Guangzhou, China ; Kong Rui

In the paper, we applied the idea of kernel-based learning methods to K-means clustering. We propose a fast and effective algorithm of kernel K-means clustering. The idea of the algorithm is that we firstly map the data from their original space to a high dimensional space (or kernel space) where the data are expected to be more separable. Then we perform K-means clustering in the high dimensional kernel space. Meanwhile we improve speed of the algorithm by using a new kernel function-conditionally positive definite kernel (CPD). The performance of new algorithm has been demonstrated to be superior to that of K-means clustering algorithm by our experiments on artificial and real data.

Published in:

Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on

Date of Conference:

16-18 Jan. 2013