Abstract
AbstractFor 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.KeywordsHard c-means clusteringFuzzy c-means clusteringK-harmonic means clustering
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
