Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

Masuyama, Naoki and Loo, Chu Kiong and Ishibuchi, Hisao and Kubota, Naoyuki and Nojima, Yusuke and Liu, Yiping (2019) Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning. IEEE Access, 7. pp. 76920-76936. ISSN 2169-3536, DOI

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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 measure, a node insertion criterion, and an edge creation criterion. Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. Moreover, generated topological networks cannot represent the distribution of data. In contrast, the proposed algorithm realizes a stable computation and reduces the number of parameters compared to existing algorithms. Furthermore, improving the ability to express the data structure more appropriately by the topological network, a mechanism that adaptively controls the node insertion criterion is introduced to the proposed algorithm. The experimental results showed that the proposed algorithm has superior performance with respect to the self-organizing and the classification abilities compared with the state-of-the-art topological clustering algorithms. © 2013 IEEE.

Item Type: Article
Uncontrolled Keywords: Adaptive resonance theory; correntropy; information theoretic learning; topological clustering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 19 Mar 2020 03:55
Last Modified: 19 Mar 2020 03:55

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