By Topic

Application of an improved K-means algorithm in gene expression data analysis

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)
Qian Ren ; Sch. of Sci., Beijing Univ. of Posts & Commun., Beijing, China ; Xinjian Zhuo

K-means algorithm is one of the most classic partition algorithms in clustering algorithms. The result obtained by K-means algorithm varies with the choice of the initial clustering centers. Motivated by this, an improved K-means algorithm is proposed based on the Kruskal algorithm, which is famous in graph theory. The procedure of this algorithm is shown as follows: Firstly, the minimum spanning tree (MST) of the clustered objects is obtained by using Kruskal algorithm. Then K-1 edges are deleted based on weights in a descending order. At last, the average values of the objects contained by the k-connected graphs resulting from last two steps are regarded as the initial clustering centers to cluster. Make the improved K-means algorithm used in gene expression data analysis, simulation experiment shows that the improved K-means algorithm has a better clustering effect and higher efficiency than the traditional one.

Published in:

Systems Biology (ISB), 2011 IEEE International Conference on

Date of Conference:

2-4 Sept. 2011