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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Main Author:	Gyemin Lee
Format:	Article
Language:	English
Published:	MDPI AG 2015-07-01
Series:	Symmetry
Subjects:	hierarchical clustering one-class support vector machines dendrogram spanning tree Gaussian kernel
Online Access:	http://www.mdpi.com/2073-8994/7/3/1164

id	doaj-d31255c3a36c45f38be5215afc85daa7
record_format	Article
spelling	doaj-d31255c3a36c45f38be5215afc85daa72020-11-24T23:02:54ZengMDPI 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
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Gyemin Lee
spellingShingle	Gyemin Lee Hierarchical Clustering Using One-Class Support Vector Machines Symmetry hierarchical clustering one-class support vector machines dendrogram spanning tree Gaussian kernel
author_facet	Gyemin Lee
author_sort	Gyemin Lee
title	Hierarchical Clustering Using One-Class Support Vector Machines
title_short	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_sort	hierarchical clustering using one-class support vector machines
publisher	MDPI AG
series	Symmetry
issn	2073-8994
publishDate	2015-07-01
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.
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
_version_	1725634750312349696

Hierarchical Clustering Using One-Class Support Vector Machines

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