摘要
ART2网络是一种著名的聚类方法,已实际应用于诸多领域,其作用于二维空间数据,不仅存在模式漂移和向量幅度信息缺失的问题,而且难以适应不规则形态分布的空间数据的聚类。提出了一种树ART2网络模型(TART2),通过长期记忆(LTM)模式的调整和向量幅度信息的学习,使ART2网络保持了带空间距离约束的旧模式记忆;引入树结构优化,降低了警戒参数设置的主观要求,减少了模式交混现象的发生。对比实验结果表明,TART2网络更适用于带状分布的空间数据聚类,具有较高的可塑性和自适应性。
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.
出处
《计算机应用》
CSCD
北大核心
2011年第5期1328-1330,共3页
journal of Computer Applications
基金
国家自然科学基金资助项目(4090119740337055)
云南省自然科学基金资助项目(2008D032M)
关键词
空间聚类
ART2神经网络
模式交混
数据粒度
树结构
spatial clustering
Adaptive Resonance Theory 2（ART2） neural network
pattern mixing
data granularity
tree-structure
