Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). Specifically, the proposed algorithm utilizes the correntropy induced metric (CIM) for defining a similarity measur...

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Main Authors: Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping
格式: Article
出版: Institute of Electrical and Electronics Engineers 2019
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在线阅读:http://eprints.um.edu.my/24041/
https://doi.org/10.1109/ACCESS.2019.2921832
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