Publication Type
Conference Paper
Book Title
Computational Science – ICCS 2009
Publication Date
Page Numbers
375 to 384
Volume
5545/200
Publisher Location
New York, New Jersey, United States of America
Conference Name
International Conference on Computational Science (Data Minining for Earth Sciences Workshop)
Conference Location
Baton Rouge, Louisiana, United States of America
Conference Sponsor
LSU and UTK
Conference Date
-
Abstract
Remote sensing data plays a key role in understanding the complex geographic phenomena. Clustering is a useful tool in discovering interesting patterns and structures within the multivariate geospatial data. One of the key issues in clustering is the speci cation of appropriate number of clusters, which is not obvious in many practical situations. In this paper we provide an extension of G-means algorithm which automatically learns the number of clusters present in the data and avoids over estimation of the number of clusters. Experimental evaluation on simulated and remotely sensed image data shows the effectiveness of our algorithm.