PROJECT TITLE :
Approximate distributed clustering by learning the confidence radius on Fisher discriminant ratio
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 arrogance radius based mostly on the info distribution in every native peer. Experimental results show that the proposed approach can achieve higher clustering accuracies than the DFEKM algorithm while preserving much lower bandwidth consumptions.
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