Abstract
A novel fuzzy clustering algorithm, called kernel possibilistic c-means model (KPCM), is proposed. KPCM algorithm is based on kernel methods and possibilistic c-means (PCM) algorithm and it is the extension of PCM algorithm. Different from PCM and FCM which are based on Euclidean distance, the proposed model is based on kernel-induced distance by using kernel methods. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where possibilistic c-means clustering is carried out. FCM, PCM and KPCM are performed numerical experiments on data sets. The experimental results show the better performance of KPCM
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.