Cart (Loading....) | Create Account
Close category search window
 

A Game Theoretic Approach for Simultaneous Compaction and Equipartitioning of Spatial Data 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

2 Author(s)
Gupta, U. ; Office of Decision Support, Univ. of South Florida, Tampa, FL, USA ; Ranganathan, N.

Data and object clustering techniques are used in a wide variety of scientific applications such as biology, pattern recognition, information systems, etc. Traditionally, clustering methods have focused on optimizing a single metric, however, several multidisciplinary applications such as robot team deployment, ad hoc networks, facility location, etc., require the simultaneous examination of multiple metrics during clustering. In this paper, we propose a novel approach for spatial data clustering based on the concepts of microeconomic theory, which can simultaneously optimize both the compaction and the equipartitioning objectives. The algorithm models a multistep, normal form game consisting of randomly initialized clusters as players that compete for the allocation of data objects from resource locations. A Nash-equilibrium-based methodology is used to derive solutions that are socially fair for all the players. After each step, the clusters are updated using the KMeans algorithm, and the process is repeated until the stopping criteria are satisfied. Extensive simulations were performed on several real data sets as well as artificially synthesized data sets to evaluate the efficacy of the algorithm. Experimental results indicate that the proposed algorithm yields significantly better results as compared to the traditional algorithms. Further, the proposed algorithm yields a high value of fairness, a metric that indicates the quality of the solution in terms of simultaneous optimization of the objectives. Also, the sensitivity of the various design parameters on the performance of our algorithm is analyzed and reported.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:22 ,  Issue: 4 )

Date of Publication:

April 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.