Divisive hierarchical clustering based on adaptive resonance theory

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.

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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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