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A novel subtractive clustering by using k-harmonic means clustering for initialization

Subtractive clustering and k-harmonic means clustering are two of the popular clustering algorithms. However, k-harmonic means need generate some initial centers for its initialization, and subtractive clustering do not need the initialization. Therefore, subtractive clustering often cannot gain the better clustering performance. In this paper, a novel subtractive clustering is proposed. The new method increase the initialization procedure for subtractive clustering and k-harmonic means is applied to the initialization. Experimental results show that our proposed method can acquire the better clustering performance than the traditional subtractive clustering, k-harmonic means and k-means.