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基于集群的增量分布式RSOM聚类方法 被引量：5

Cluster-Computer Based Incremental and Distributed RSOM Data-Clustering
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摘要 对于海量和高维的大规模数据聚类问题,其数据个数以及模式种类通常处于一个动态增加的过程之中,为此进行增量、并行算法的设计,以提供更好的计算能力是十分必要的.注意到人脑增量学习的本质和RSOM(Re-cursive Self-Organizing Map)的层次化、分布式结构特点,本文研究了基于高性能集群并行计算环境的增量、分布式RSOM并行算法,并以视频图像特征集实例证实了算法的可行性. For large data-set with high dimeusionality, of which the numbers of samples and patterns increase dynamically, in roder to improve the computing-efficiency, it is necessary to design parallel incremental clustering algorithm. Noticing the nature of the human brain-an incremental studying style, and the hierarchical and distributed structure properties of a RSOM tree, a Cluster- computer system based incremental and distributed parallel algorithm of RSOM tree is proposed. The performance of this method is tested with the large feature data sets which are extracted from a large amount of video pictures.
作者 夏胜平 刘建军 袁振涛 虞华 张乐锋 郁文贤
机构地区 国防科学技术大学电子科学与工程学院ATR重点实验室
出处 《电子学报》 EI CAS CSCD 北大核心 2007年第3期385-391,共7页 Acta Electronica Sinica
基金 国家863计划(No.2003AA134030) 国家重点实验室基金项目(No.9140C8001020603)
关键词 数据聚类 增量 分布式并行计算 RSOM(Reeursive SELF-ORGANIZING Map) 集群系统 data clustering incremental distributed parallel computing RSOM （Recursive Serf-Organizing Map） cluster system
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献19

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共引文献15

同被引文献67

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引证文献5

二级引证文献6

电子学报
2007年 第3期

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