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基于归一化距离的结构聚类分析
引用本文:唐旭清,朱平,程家兴.基于归一化距离的结构聚类分析[J].模式识别与人工智能,2009,22(5).
作者姓名:唐旭清  朱平  程家兴
作者单位:1. 江南大学理学院,无锡,214122
2. 安徽大学,国家教育部智能计算与信号处理重点实验室,合肥,230039
基金项目:国家自然科学基金,国家重点基础研究发展规划(973计划),教育部高等学校博士学科点专项科研基金,科技部国际科技合作项目
摘    要:在有序粒度空间理论的基础上,提出基于归一化距离的结构聚类(分类)分析理论和方法研究.首先,提出依距离的一致聚类的概念,给出有序粒度空间的结构聚类特征研究.其次,给出基于归一化距离结构聚类分析完整的理论研究,获得基于归一化距离结构聚类的算法.再给出基于粒度空间的最佳聚类问题研究,提出基于粒度空间的、获取最佳聚类的方法,并且这一方法具备全局最优性质.最后,给出基于归一化距离空间的结构聚类的融合技术的研究,即通过两个归一化距离的交运算获取结构聚类融合的研究方法.这些结论为基于距离的结构聚类(分类)提供一整套理论和方法.

关 键 词:有序粒度空间  一致聚类  归一化距离  最佳聚类  结构聚类融合

Analysis of Structural Clustering Based on Normalized Metric
TANG Xu-Qing,ZHU Ping,CHENG Jia-Xing.Analysis of Structural Clustering Based on Normalized Metric[J].Pattern Recognition and Artificial Intelligence,2009,22(5).
Authors:TANG Xu-Qing  ZHU Ping  CHENG Jia-Xing
Affiliation:TANG Xu-Qing1,ZHU Ping1,CHENG Jia-Xing2 1(School of Science,Jiangnan University,Wuxi 214122)2(Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education,Anhui University,Hefei 230039)
Abstract:On the basis of the ordered granular space, structural clustering (or classification) analysis is proposed based on normalized metric. Firstly, the concept of consistent clustering according to metric is presented, and the research on consistent clustering characteristic of ordered granular space is given. Secondly, structural clustering analysis theory is given based on normalized metric, and the algorithm to obtain its structural clustering is discussed. Thirdly, the research on the determination of the optimal clustering based on ordered granular space is carried out. A method to obtain the optimal clustering is given, and the method is global optimal. Finally, the fusion technology based on structural clusters of normalized metrics is studied by the intersection operation of two normalized metrics. The conclusions provide a comprehensive theory and methodology on structural clustering (or classification) analysis hased on metric.
Keywords:Ordered Granular Space  Consistent Clustering  Normalized Metric  Optimal Clustering  Structural Clustering Fusion
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