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...
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Artikel |
| Veröffentlicht: |
Institute of Electrical and Electronics Engineers
2019
|
| Schlagworte: |
| _version_ | 1825722174253563904 |
|---|---|
| author | Masuyama, Naoki Loo, Chu Kiong Ishibuchi, Hisao Kubota, Naoyuki Nojima, Yusuke Liu, Yiping |
| author_facet | Masuyama, Naoki Loo, Chu Kiong Ishibuchi, Hisao Kubota, Naoyuki Nojima, Yusuke Liu, Yiping |
| author_sort | Masuyama, Naoki |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T06:01:37Z |
| format | Article |
| id | um.eprints-24041 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:01:37Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | um.eprints-240412020-03-19T03:55:59Z http://eprints.um.edu.my/24041/ Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning Masuyama, Naoki Loo, Chu Kiong Ishibuchi, Hisao Kubota, Naoyuki Nojima, Yusuke Liu, Yiping QA75 Electronic computers. Computer science 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. Institute of Electrical and Electronics Engineers 2019 Article PeerReviewed 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 https://doi.org/10.1109/ACCESS.2019.2921832 <https://doi.org/10.1109/ACCESS.2019.2921832>. https://doi.org/10.1109/ACCESS.2019.2921832 doi:10.1109/ACCESS.2019.2921832 |
| spellingShingle | QA75 Electronic computers. Computer science Masuyama, Naoki Loo, Chu Kiong Ishibuchi, Hisao Kubota, Naoyuki Nojima, Yusuke Liu, Yiping Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning |
| title | Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning |
| title_full | Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning |
| title_fullStr | Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning |
| title_full_unstemmed | Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning |
| title_short | Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning |
| title_sort | topological clustering via adaptive resonance theory with information theoretic learning |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT masuyamanaoki topologicalclusteringviaadaptiveresonancetheorywithinformationtheoreticlearning AT loochukiong topologicalclusteringviaadaptiveresonancetheorywithinformationtheoreticlearning AT ishibuchihisao topologicalclusteringviaadaptiveresonancetheorywithinformationtheoreticlearning AT kubotanaoyuki topologicalclusteringviaadaptiveresonancetheorywithinformationtheoreticlearning AT nojimayusuke topologicalclusteringviaadaptiveresonancetheorywithinformationtheoreticlearning AT liuyiping topologicalclusteringviaadaptiveresonancetheorywithinformationtheoreticlearning |