By Topic

An Improved Partitioning-Based Web Documents Clustering Method Combining GA with ISODATA

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

5 Author(s)
Zhengyu Zhu ; Chongqing Univ., Chongqing ; Yunyan Tian ; Jingqiu Xu ; Xin Deng
more authors

The existing partitioning-based clustering algorithms, such as k-means, k-medoids and their variations, are simple in theory and fast in convergence speed, but they always just reach local optimum when the iterations terminate and they are not suitable for discovering clusters in the cases when their sizes are very different. This paper proposes an improved Web documents clustering method, using genetic algorithm (GA) which introduces some ideas of ISODATA [6] into the design of its mutation operation. Experiments show that the GA's global search characteristic can avoid local optimum and the ISODATA-based mutation operation makes the improved clustering algorithm have the self-adjusting ability to discover clusters of different sizes.

Published in:

Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on  (Volume:2 )

Date of Conference:

24-27 Aug. 2007