Support Vector Clustering of Microarray Gene Expression Data

In this paper Gaussian kernel approach will be explored for microarray gene expression data using support vector clustering (SVC). SVC uses the idea of support vector machines. The data points are mapped to a high dimensional feature space with a kernel function, and a minimal enclosing sphere in looked for. Cluster boundaries in data space are complex shapes and are formed from the sphere boundary points in the feature space. The performance of the algorithm and the biological implications will be demonstrated as a future work.