Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

Abstract:

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 1934-1938
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.1934
• Online since: March 2014
• Authors: Ming Xiao*
• Keywords: ART2 Neural Network, Spatial Clustering, Tree-Structure

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