Support vector clustering combined with spectral graph partitioning

We propose a new support vector clustering (SVC) strategy by combining (SVC) with spectral graph partitioning (SGP). SVC has two main steps: support vector computation and cluster labeling using adjacency matrix. Spectral graph partitioning (SGP) method is applied to the adjacency matrix to determine the cluster labels. It is feasible to combine multiple adjacency matrices computed using different parameters. A novel multi-resolution combination method is proposed for cluster labeling using the SGP for the purpose of boosting the clustering performance.