Finding Clusters using Support Vector Classifiers

摘要

This paper shows how clustering can be performed by using support vector classifiers and model selection. We introduce a heuristic method for non-parametric clustering that uses support vector classifiers for finding support vectors describing portions of clusters and uses a model selection criterion for joining these portions. Clustering is viewed as a two-class classification problem and a soft-margin support vector classifier is used for separating clusters from other points suitably sampled in the data space. The method is tested on five real life data sets, including microarray gene expression data and array-CGH data.