Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Neural Field Theory for Motion Perception
Dynamic Neural Field Theory for Motion Perception
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Dynamic Cluster Formation Using Level Set Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Local excitation solutions in one-dimensional neural fields by external input stimuli
Neural Computing and Applications
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Dynamics of feature categorization
Neural Computation
Spontaneous clustering via minimum gamma-divergence
Neural Computation
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In this letter, we present a new hierarchical clustering approach based on the evolutionary process of Amari's dynamical neural field model. Dynamical neural field theory provides a theoretical framework macroscopically describing the activity of neuron ensemble. Based on it, our clustering approach is essentially close to the neurophysiological nature of perception. It is also computationally stable, insensitive to noise, flexible, and tractable for data with complex structure. Some examples are given to show the feasibility.