By Topic

A hybrid clustering algorithm based on grid density and rough sets

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)
Lv Huigang ; Inst. of Eng., Air Force Eng. Univ., Xi''an ; Teng Peng ; Huang Jun ; Zhang Fengming

According to the characters of dynamic and SOM clustering algorithm, propose a novel clustering method, rough dynamic clustering based on grid-density algorithm (GDRDC). The algorithm contains initial clustering stages and precise adjustment stages. During switch from the first stage to second stage, according to rough sets idea, class kernel and freedom point sets base on grid-density are determined, and though which the two stages are joined. Then making farther adjustment by dynamic clustering method, the final clustering result is get. The experiment result shows that it is better than SOM and K-means, especially for nonlinear separable data.

Published in:

Control Conference, 2008. CCC 2008. 27th Chinese

Date of Conference:

16-18 July 2008