Divisive hierarchical clustering based on adaptive resonance theory
Document Type
Conference Item
Publication Date
1-1-2020
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.
Keywords
Divisive hierarchical clustering, Color quantization, Adaptive resonance theory
Divisions
ai
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)
Publisher
IEEE
Publisher Location
345 E 47TH ST, NEW YORK, NY 10017 USA
Event Title
2020 International Symposium on Community-Centric Systems (CCS)
Event Location
Tokyo, Japan
Event Dates
23-26 September 2020
Event Type
conference
Additional Information
International Symposium on Community-Centric Systems (CcS), Tokyo, Japan, Sep 23-26, 2020