Divisive Hierarchical Clustering Based on Adaptive Resonance Theory

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.

[1]  Stephen Grossberg,et al.  Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..

[2]  Ezequiel López-Rubio,et al.  The Growing Hierarchical Neural Gas Self-Organizing Neural Network , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[4]  Christopher F. Parmeter,et al.  Normal reference bandwidths for the general order, multivariate kernel density derivative estimator , 2012 .

[5]  Hisao Ishibuchi,et al.  Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric , 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

[6]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[7]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[8]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Chu Kiong Loo,et al.  Kernel Bayesian ART and ARTMAP , 2018, Neural Networks.

[10]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[11]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[12]  Hisao Ishibuchi,et al.  Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning , 2019, IEEE Access.

[13]  Daniel Boley,et al.  Principal Direction Divisive Partitioning , 1998, Data Mining and Knowledge Discovery.

[14]  Chu Kiong Loo,et al.  A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure , 2019, Int. J. Neural Syst..

[15]  Boaz Lerner,et al.  The Bayesian ARTMAP , 2007, IEEE Transactions on Neural Networks.

[16]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[17]  Giansalvo Cirrincione,et al.  The GH-EXIN neural network for hierarchical clustering , 2020, Neural Networks.