By Topic

A New Measure of Stability of Clustering Solutions: Application to Data Partitioning

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

2 Author(s)
Saha, S. ; Machine Intell. Unit, Indian Stat. Inst., Kolkata, India ; Bandyopadhyay, S.

In this paper at first a new measure of stability of clustering solutions over different bootstrap samples of a data set is proposed. Thereafter in this paper, a multiobjective optimization based clustering technique is developed which optimizes both the measures of symmetry and stability simultaneously to automatically determine the appropriate number of clusters and the appropriate partitioning from data sets having symmetrical shaped clusters. The proposed algorithm utilizes a recently developed simulated annealing based multiobjective optimization technique, AMOSA, as the underlying optimization method. Here assignment of points to different clusters are done based on a recently developed point symmetry based distance rather than the Euclidean distance. Results on several artificial and real-life data sets show that the proposed technique is well-suited to detect the number of clusters from data sets having point symmetric clusters.

Published in:

Adaptive and Intelligent Systems, 2009. ICAIS '09. International Conference on

Date of Conference:

24-26 Sept. 2009