Locally weighted support vector clustering

Support vector clustering (SVC) has been successfully used to achieve robust clustering. SVC has the capability of dealing with noise and outliers, and can identify clusters of arbitrary shapes. Moreover, SVC has its superiorities for automatically obtaining the number of clusters and having no requirement for prior knowledge to determine the system topological structure. However, in most cases, it is not possible to choose the best of input parameter values for an input dataset which restricts the application of SVC. In this paper, we propose a support vector clustering approach termed locally weighted support vector clustering, which tackles the automatic parameter estimation problem for SVC. An exponential weighting to each data point is assigned to reflect the within-class importance of different data points in order to detect outliers and noise efficiently. The experiments on multiple real-world datasets demonstrate the effectiveness of our approach.