关键词: 空间聚类, ART2神经网络, 模式交混, 数据粒度, 树结构

Abstract: The Adaptive Resonance Theory 2 (ART2) is one of well-known clustering algorithms and has been applied to many fields practically. However, to be a clustering algorithm for two-dimension spatial data, it not only has the shortcomings of pattern drift and vector model of information missing, but also is difficult to adapt to spatial data clustering of irregular distribution. A Tree-ART2 (TART2) network model was proposed. It retained the memory of old model which maintained the constraint of spatial distance by learning and adjusting Long Time Memory (LTM) pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model could reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. The comparative experimental results show that TART2 network is suitable for clustering about the ribbon distribution of spatial data, and it has higher plasticity and adaptability.

Key words: spatial clustering, Adaptive Resonance Theory 2 (ART2) neural network, pattern mixing, data granularity, tree-structure