Abstract
Twin support vector clustering (TWSVC) algorithm has attracted a growing focus in recent years. In this paper, an enhanced TWSVC algorithm is proposed by using particle swarm optimization (PSO) for obtaining k-cluster planes and assigning each data sample to a correct cluster. This enhanced algorithm basically uses TWSVC to refine the clusters formed by PSO. The hybrid PSO-TWSVC algorithm is evaluated on a collected cobalt dataset in the nanotechnology field and then tested on four public datasets. The experiments of hybrid PSO-TWSVC with the classical K-means and TWSVC algorithms have proved that the hybrid PSO-TWSVC clustering algorithm has obvious advantages on accuracy.
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