Name
Abstract | ||
|---|---|---|
One of the practical issues in clustering is the specification of the appropriate number of clusters, which is not obvious when analyzing geospatial datasets, partly because they are huge (both in size and spatial extent) and high dimensional. In this paper we present a computationally effcient model-based split and merge clustering algorithm that incrementally finds model parameters and the number of clusters. Additionally, we attempt to provide insights into this problem and other data mining challenges that are encountered when clustering geospatial data. The basic algorithm we present is similar to the G-means and X-means algorithms; however, our proposed approach avoids certain limitations of these well-known clustering algorithms that are pertinent when dealing with geospatial data. We compare the performance of our approach with the G-means and X-means algorithms. Experimental evaluation on simulated data and on multispectral and hyperspectral remotely sensed image data demonstrates the effectiveness of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.procs.2011.04.020 | Procedia Computer Science |
Keywords | Field | DocType |
Clustering,EM,GMM,K-means,G-means,X-means | Fuzzy clustering,Canopy clustering algorithm,Data mining,Clustering high-dimensional data,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Machine learning | Journal |
Volume | ISSN | Citations |
4 | 1877-0509 | 2 |
PageRank | References | Authors |
0.44 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ranga R. Vatsavai | 1 | 20 | 3.38 |
| Christopher T. Symons | 2 | 189 | 13.66 |
| Varun Chandola | 3 | 2888 | 120.50 |
| Goo Jun | 4 | 150 | 11.55 |