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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.