Scheduled System Maintenance:
On Wednesday, July 29th, IEEE Xplore will undergo scheduled maintenance from 7:00-9:00 AM ET (11:00-13:00 UTC). During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

A rough set based clustering algorithm and the information theoretical approach to refine clusters

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)
Qingdong Wang ; Nat. Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China ; Huaping Dai ; Youxian Sun

In many clustering processes, the presence of more information does not usually generate a corresponding increase in the performance of clustering. The presence of irrelevant information decreases the effectiveness of the clustering algorithm. We propose a solution to improve the quality of clustering that is an attribute-weighted clustering algorithm based on rough set theory and the information theoretical refinement process. Firstly, we give every attribute the same weight value, and use rough set based clustering algorithm to get the initial classes. Then we weigh every attribute by Shannon's Entropy Theory, substitute mutual entropy values for the weight of every attribute, and compute with attribute-weighted rough set clustering algorithm again to refine and improve the clustering result. We have tested our algorithm on data sets from UCI repository. The experimental results show that our algorithm can obtain better results in classification rate and purity of classes than other traditional clustering methods.

Published in:

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on  (Volume:5 )

Date of Conference:

15-19 June 2004