Name
Abstract | ||
|---|---|---|
Support Vector Clustering has gained reasonable attention from the researchers in exploratory data analysis due to firm theoretical foundation in statistical learning theory. Hard Partitioning of the data set achieved by support vector clustering may not be acceptable in real world scenarios. Rough Support Vector Clustering is an extension of Support Vector Clustering to attain a soft partitioning of the data set. But the Quadratic Programming Problem involved in Rough Support Vector Clustering makes it computationally expensive to handle large datasets. In this paper, we propose Rough Core Vector Clustering algorithm which is a computationally efficient realization of Rough Support Vector Clustering. Here Rough Support Vector Clustering problem is formulated using an approximate Minimum Enclosing Ball problem and is solved using an approximate Minimum Enclosing Ball finding algorithm. Experiments done with several Large Multi class datasets such as Forest cover type, and other Multi class datasets taken from LIBSVM page shows that the proposed strategy is efficient, finds meaningful soft cluster abstractions which provide a superior generalization performance than the SVM classifier. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-77046-6_37 | PReMI |
Keywords | Field | DocType |
exploratory data analysis,quadratic program | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Canopy clustering algorithm,Clustering high-dimensional data,Data stream clustering,Pattern recognition,Correlation clustering,Constrained clustering,Machine learning | Conference |
Volume | ISSN | ISBN |
4815 | 0302-9743 | 3-540-77045-3 |
Citations | PageRank | References |
2 | 0.40 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seshikanth Varma | 1 | 3 | 0.79 |
| S. Asharaf | 2 | 122 | 13.07 |
| M. Narasimha Murty | 3 | 824 | 86.07 |