Please use this identifier to cite or link to this item: https://doi.org/10.1049/el.2012.0347
Title: Approximate distributed clustering by learning the confidence radius on Fisher discriminant ratio
Authors: Shen, X.J.
Zha, Z.J. 
Zhu, Q.
Yang, H.B.
Gu, P.Y.
Issue Date: 2012
Source: Shen, X.J., Zha, Z.J., Zhu, Q., Yang, H.B., Gu, P.Y. (2012). Approximate distributed clustering by learning the confidence radius on Fisher discriminant ratio. Electronics Letters 48 (14) : 839-841. ScholarBank@NUS Repository. https://doi.org/10.1049/el.2012.0347
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. © 2012 The Institution of Engineering and Technology.
Source Title: Electronics Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/39058
ISSN: 00135194
DOI: 10.1049/el.2012.0347
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