Abstract
Abstract In this paper, a multi-sphere support vector clustering algorithm (SHMSVC) is proposed, which is based on statistical histogram, suitable for large data set, robust to noise and outliers, and able to automatically determine the number of clusters, and identify data points with arbitrary contours of cluster boundaries. The proposed SHMSVC algorithm consists of two major phases. In the first phase, a large data set is transformed into a small set of grid points by statistical histogram and median filter. Based on the connectivity of a multi-dimensional binary image, the set of grid points is classified. In the second phase, the support vector domain description (SVDD) algorithm is performed several times for each cluster. The memberships of each data point in original data set are computed, and the final clustering result is found based on these computed memberships. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm.
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