Clustering methods based on rough estimate of cluster core
We present a Condensation Nucleus Clustering (CNC) method based on our study of SVC algorithm. In CNC, data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. We consider the inner data images as the rough estimate of each cluster's core, and they can be easily clustered. We name the groups of cluster result as Condensation Nucleus. Then assign the remaining data points into each cluster by linear discriminant analysis. Furthermore, we improve the CNC method to GCNC (Gradational Condensation Nucleus Clustering). In GCNC, the remaining data are assigned to each cluster gradationally. With the Condensation Nucleus bigger and the remaining data less, the Condensation Nucleus grow up to the final cluster results. We compare our methods with other similar clustering algorithm to demonstrate the performance of the proposed method on several datasets.