Abstract
Presented is a new clustering algorithm with approximate distributed clustering over a peer-to-peer (P2P) network. The Fisher discriminant ratio is used to dynamically learn the confidence radius based on the data distribution in every local peer. Experimental results show that the proposed approach can achieve better clustering accuracies than the DFEKM algorithm while preserving much lower bandwidth consumptions.
Sign-in/Register to access full text options 
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.
