By Topic

The optimization arithmetic of K-means clustering based on Indirect Feature Weight Learning

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)
Bin Zeng ; Sch. of Inf. Eng., Zhejiang Forestry Univ., Lin''an, China ; Wei Zhao ; Chao Luo ; Benyue Chen

The performance of K-means clustering algorithm depended on the selection of distance metrics, there was a problem with Dimension Trap. Using the feature learning parameter can solve this problem, but the choice of feature learning was difficult, so the improper choice of feature learning would affect the convergence speed of clustering algorithm, even leading to non-convergence. In regard to the choice of feature learning, a new clustering method is discussed. The method of feature learning Indirect Feature Weight Learning automatically is adopted to protect more rapid convergence and improve the clustering performance. The result in testing data in typical UCI machine learning repository indicate that these measures have improved clustering performance.

Published in:

Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On  (Volume:2 )

Date of Conference:

12-13 June 2010