Weighted principal component analysis for interval-valued data based on fuzzy clustering

被引：0
|
作者：
Sato-Ilic, M [1 ]
机构：
[1] Univ Tsukuba, Inst Policy & Planning Sci, Tsukuba, Ibaraki 305, Japan
来源：
2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS | 2003年
关键词：
uncertainty; risk; symbolic data analysis; PCA;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a weighted principal component analysis (WPCA) for interval-valued data using the result of fuzzy clustering. In this method, we introduce two data structures which are classification structure and principal component structure. One of them is used for weights and the other is used for the analysis of itself. So, we can reduce the risk of a wrong assumption of the introduced data structure, comparing the conventional method which assumes only one data structure on the observation.
引用
收藏
页码：4476 / 4482
页数：7
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