Some Notes on K-Harmonic Means Clustering Algorithm

For K-harmonic means(KHM) clustering algorithm and its generalized form: KHM P . clustering algorithm, fuzzy c-means clustering algorithm (FCM) and its generalized form: GFCM P clustering algorithms, the relations between KHM and FCM, KHM P and GFCM P are studied. By using the reformulation of the GFCM P , the facts that KHM P is a special case of FCM P as fuzzy parameter m is 2 and parameterp is greater than 2, and KHM is FCM as fuzzy parameter m is 2 are revealed. By using the theory of Robust Statistics, the performances of FCM P under different parameter p is studied and the conclusions are obtained: GFCM p is sensitive to noise when parameter p is greater than 1; it is robust to noise when p is less than 1. Experimental results show the correctness of our analysis.