By Topic

K-means clustering with multiresolution peak detection

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)
Guanshan Yu ; Dept. of Comput. Sci. & Eng., Nebraska-Lincoln Univ., Lincoln, NE ; Leen-Kiat Soh ; Bond, A.

Clustering is a practical data mining approach of pattern detection. Because of the sensitivity of initial conditions, k-means clustering often suffers from low clustering performance. We present a procedure to refine initial conditions of k-means clustering by analyzing density distributions of a data set before estimating the number of clusters k necessary for the data set, as well as the positions of the initial centroids of the clusters. We demonstrate that this approach indeed improves the accuracy and performance of k-means clustering measured by average intra to inter-clustering error ratio. This method is applied to the virtual ecology project to design a virtual blue jay system

Published in:

Electro Information Technology, 2005 IEEE International Conference on

Date of Conference:

22-25 May 2005