Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
The Journal of Machine Learning Research
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
| Hi-index | 0.00 |
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.