Analysis of Principal Component Cluster Analysis under Extreme Situations

机译：极端情况下的主成分聚类分析

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A discussion about the invalidations of canonical cluster analysis and ordinary principal component cluster analysis under extreme situations is presented in this paper,by defining an objective weighted principal component distance as classification statistic,this paper solves the problems effectively which can't be revealed by principal component cluster analysis under extreme situations,and the empirical test proves its validation.

机译：讨论了极端情况下规范聚类分析和普通主成分聚类分析的无效性，通过定义客观加权主成分距离作为分类统计量，有效解决了主成分无法揭示的问题。极端情况下的组件聚类分析，并通过经验检验证明了其有效性。

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来源
《2012 IEEE 5th International conference on management engineering amp; technology of statistics》|2012年|779-782|共4页
会议地点 Qingdao(CN)
作者
Jianping Zhu; Deqing Wang; Wanchuang Zhu;
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作者单位

Data Mining Research Center of Xiamen University School of Economics Xiamen University Xiamen,China;

Data Mining Research Center of Xiamen University School of Economics Xiamen University Xiamen,China;

Data Mining Research Center of Xiamen University School of Economics Xiamen University Xiamen,China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
Classification; Principal Component Cluster Analysis; Extreme Situations;

机译：分类;主成分聚类分析；极端情况;

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Analysis of Principal Component Cluster Analysis under Extreme Situations

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