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 |
| _version_ | 1811184288549306368 |
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| author | Gyemin Lee |
| author_facet | Gyemin Lee |
| author_sort | Gyemin Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:10:52Z |
| format | Article |
| id | doaj.art-d31255c3a36c45f38be5215afc85daa7 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:10:52Z |
| publishDate | 2015-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-d31255c3a36c45f38be5215afc85daa72022-12-22T04:22:36ZengMDPI AGSymmetry2073-89942015-07-01731164117510.3390/sym7031164sym7031164Hierarchical Clustering Using One-Class Support Vector MachinesGyemin Lee0Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology (SeoulTech), 232 Gongneung-ro Nowon-gu, Seoul 139743, KoreaThis 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.http://www.mdpi.com/2073-8994/7/3/1164hierarchical clusteringone-class support vector machinesdendrogramspanning treeGaussian kernel |
| spellingShingle | Gyemin Lee Hierarchical Clustering Using One-Class Support Vector Machines Symmetry hierarchical clustering one-class support vector machines dendrogram spanning tree Gaussian kernel |
| title | Hierarchical Clustering Using One-Class Support Vector Machines |
| title_full | Hierarchical Clustering Using One-Class Support Vector Machines |
| title_fullStr | Hierarchical Clustering Using One-Class Support Vector Machines |
| title_full_unstemmed | Hierarchical Clustering Using One-Class Support Vector Machines |
| title_short | Hierarchical Clustering Using One-Class Support Vector Machines |
| title_sort | hierarchical clustering using one class support vector machines |
| topic | hierarchical clustering one-class support vector machines dendrogram spanning tree Gaussian kernel |
| url | http://www.mdpi.com/2073-8994/7/3/1164 |
| work_keys_str_mv | AT gyeminlee hierarchicalclusteringusingoneclasssupportvectormachines |