By Topic

A study on feature extraction of parallel immune genetic clustering algorithm based on clustering center optimization

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

4 Author(s)
Juan Zou ; Inf. Eng. Coll., Xiangtan Univ., Xiangtan, China ; Jinhua Zheng ; Jingye Zhou ; Cheng Deng

A method of feature extraction of parallel immune genetic clustering algorithm based on clustering center optimization is put forward which is using the characteristics of text. The different characteristics of between the features is give full consideration by this method, and the parallel and immune mechanisms genetic algorithm is used which can calculate clustering center of the feature. Comparative test results show that the method not only can reduce the dimension of the feature, but also can increase the correct rate and recall rate of classification, thus the overall performance of the classification system is enhanced, and it can be enable the system to achieve a higher level of automation and strong portability.

Published in:

Natural Computation (ICNC), 2010 Sixth International Conference on  (Volume:5 )

Date of Conference:

10-12 Aug. 2010