Hierarchical Clustering Using One-Class Support Vector Machines
This paper presents a novel hierarchical clustering method using support vector machines. A common approach for hierarchical clustering is to use distance for the task. However, different choices for computing inter-cluster distances often lead to fairly distinct clustering outcomes, causing interpr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-07-01
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| Series: | Symmetry |
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| Online Access: | http://www.mdpi.com/2073-8994/7/3/1164 |
| Summary: | This paper presents a novel hierarchical clustering method using support vector machines. A common approach for hierarchical clustering is to use distance for the task. However, different choices for computing inter-cluster distances often lead to fairly distinct clustering outcomes, causing interpretation difficulties in practice. In this paper, we propose to use a one-class support vector machine (OC-SVM) to directly find high-density regions of data. Our algorithm generates nested set estimates using the OC-SVM and exploits the hierarchical structure of the estimated sets. We demonstrate the proposed algorithm on synthetic datasets. The cluster hierarchy is visualized with dendrograms and spanning trees. |
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| ISSN: | 2073-8994 |