Incremental Clustering Algorithm for Earth Science Data Mining

Authors:
Ranga Raju Vatsavai
Affiliations:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, USA TN 37831
Venue:
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Year:
2009

Citing 4
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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 specification 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.