Improved support vector clustering

Support vector clustering (SVC) is an important boundary-based clustering algorithm in multi applications. But SVC’s popularity is degraded by its pricy computation and poor labeling performance. Different from existing modifications that only resolve one of two bottlenecks, this paper presents an improved SVC, iSVC, to address two bottlenecks simultaneously. iSVC’s contributions are as follows: (1) It includes a reduction strategy that can help to develop clustering model on a qualified subset. The reduction strategy is based on the Schrödinger equation to find the crucial data towards model formulation. (2) The original objective is modified; it cooperates with the reduction strategy to produce the model with subtle loss of quality. (3) iSVC employs a new label approach to label data according to the geometric properties of feature space. The new approach labels data in a simple but effective way without suffering from the randomness originated in the old algorithm. (4) The geometric property is proofed to guarantee the new labeling approach’s validation. Theoretical analysis and empirical evidence suggest that iSVC overcomes two bottlenecks well. And when compared with some common clustering methods, it does a good job in performance and efficiency, which opens a broad way of applications for SVC.