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A neural architecture for hierarchical clustering

Conference Paper


Abstract


  • An hierarchical neural network structure for clustering problems is presented and a statistical analysis of its performance is conducted. This neural network architecture aims to find, through competition and cooperation, maximally related objects in a scene. The architecture was first introduced by Maren and Ali (1983), and was named the hierarchical scene structure (HSS). We propose an enhancement of the original HSS and demonstrate that this leads to an improved performance. It is also shown that further improvement in performance can be achieved by cascading two enhanced HSS networks.

Publication Date


  • 1994

Citation


  • Bouzerdoum, A., Southcott, M. L., Zhu, J., & Bogner, R. E. (1994). A neural architecture for hierarchical clustering. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2 (pp. II661-II664). doi:10.1109/ICASSP.1994.389569

Scopus Eid


  • 2-s2.0-85079276746

Start Page


  • II661

End Page


  • II664

Volume


  • 2

Abstract


  • An hierarchical neural network structure for clustering problems is presented and a statistical analysis of its performance is conducted. This neural network architecture aims to find, through competition and cooperation, maximally related objects in a scene. The architecture was first introduced by Maren and Ali (1983), and was named the hierarchical scene structure (HSS). We propose an enhancement of the original HSS and demonstrate that this leads to an improved performance. It is also shown that further improvement in performance can be achieved by cascading two enhanced HSS networks.

Publication Date


  • 1994

Citation


  • Bouzerdoum, A., Southcott, M. L., Zhu, J., & Bogner, R. E. (1994). A neural architecture for hierarchical clustering. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2 (pp. II661-II664). doi:10.1109/ICASSP.1994.389569

Scopus Eid


  • 2-s2.0-85079276746

Start Page


  • II661

End Page


  • II664

Volume


  • 2