title: A new clustering approach on the basis of dynamical neural field.
abstract: 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.
authors.affiliation: Department of Applied Mathematics, School of Science, Xi'an Jiaotong University, China. dqjin@yahoo.cn
authors.forename: Dequan
authors.initials: D
authors.lastname: Jin
authors.forename: Jigen
authors.initials: J
authors.lastname: Peng
authors.forename: Bin
authors.initials: B
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D006801:Humans; D008959:Models, Neurological; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*