Hierarchical Clustering Using One-Class Support Vector Machines

作者：Lee Gyemin^*

来源：Symmetry-Basel, 2015, 7(3): 1164-1175.

DOI：10.3390/sym7031164

• hierarchical clustering
• one-class support vector machines
• dendrogram
• spanning tree
• Gaussian kernel

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.

• 出版日期2015-9