Incremental Clustering Algorithm For Earth Science Data Mining
Conference
·
OSTI ID:979215
- ORNL
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 specication 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.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 979215
- Resource Relation:
- Conference: International Conference on Computational Science (Data Minining for Earth Sciences Workshop), Baton Rouge, LA, USA, 20090525, 20090527
- Country of Publication:
- United States
- Language:
- English
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