Hierarchical Clustering Using One-Class Support Vector Machines

Symmetry

Gyemin Lee¹ 

[1] Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology (SeoulTech), 232 Gongneung-ro Nowon-gu, Seoul 139743, Korea;

关键词: hierarchical clustering;    one-class support vector machines;    dendrogram;    spanning tree;    Gaussian kernel;   

DOI  :  10.3390/sym7031164

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown