Yamada, Yuna and Masuyama, Naoki and Amako, Narito and Nojima, Yusuke and Loo, Chu Kiong and Ishibuchi, Hisao (2020) Divisive hierarchical clustering based on adaptive resonance theory. In: 2020 International Symposium on Community-Centric Systems (CCS), 23-26 September 2020, Tokyo, Japan.
Full text not available from this repository.Abstract
Divisive hierarchical clustering is a powerful tool for extracting knowledge from data with a pluralistic and appropriate information granularity. Recent developments of hierarchical clustering algorithms apply Growing Neural Gas (GNG) to data divisive mechanisms. However, GNG-based algorithms tend to generate nodes excessively and sensitive to the input order of data points. Furthermore, the plasticity-stability dilemma is another unavoidable problem. In this paper, we propose a divisive hierarchical clustering algorithm based on Adaptive Resonance Theory-based clustering. Simulation experiments show that the proposed algorithm can generate an appropriate tree structure depending on data while improving the performance of hierarchical clustering.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Frontier Research Grant from University of Malaya (FG003-17AFR), Office of Naval Research (ONRG-NICOP-N62909-18-1-2086), International Collaboration Fund from MESTECC, Malaysia (IF0318M1006), National Natural Science Foundation of China (NSFC) (61876075) |
| Additional Information: | International Symposium on Community-Centric Systems (CcS), Tokyo, Japan, Sep 23-26, 2020 |
| Uncontrolled Keywords: | Divisive hierarchical clustering; Color quantization; Adaptive resonance theory |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 12 Apr 2023 07:09 |
| Last Modified: | 12 Apr 2023 07:09 |
| URI: | http://eprints.um.edu.my/id/eprint/37205 |
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