@inproceedings{50b53a7287c54b4884bb49d2ff5dfd43,
title = "Multi-sphere support vector clustering",
abstract = "Current support vector clustering method determines the smallest sphere that encloses the image of a dataset in feature space. This sphere when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries for the dataset. However this method does not guarantee that the single sphere and the resulting cluster boundaries can best describe the dataset if there are some distinctive data distributions in this dataset. We propose multi-sphere support vector clustering to address this issue. Data points in data space are mapped to a high dimensional feature space and a set of smallest spheres that encloses the image of the dataset is determined. This set of spheres when mapped back to data space will form a set of contours that can be interpreted as cluster boundaries. Experiments on different datasets are performed to demonstrate that the proposed approach provides a better cluster analysis than the current support vector clustering method.",
keywords = "Cluster analysis, kernel method, support vector data description, support vector machine",
author = "Trung Le and Dat Tran and Phuoc Nguyen and Wanli Ma and Dharmendra Sharma",
year = "2011",
doi = "10.1007/978-3-642-24958-7_62",
language = "English",
isbn = "9783642249570",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 2",
pages = "537--544",
editor = "Bao-Liang Lu and Liqing Zhang and James Kwok",
booktitle = "Neural Information Processing",
note = "18th International Conference on Neural Information Processing, ICONIP 2011 ; Conference date: 13-11-2011 Through 17-11-2011",
}