A Robust Clustering Algorithm for Interval Data

被引：0
作者：
Yang, Miin-Shen [1 ]
Kuo, Hsien-Chun [1 ]
Hung, Wen-Liang [2 ]
机构：
[1] Chung Yung Christian Univ, Dept Appl Math, Chungli 32023, Taiwan
[2] Natl Hsinchu Univ Educ, Grad Inst Comp Sci, Hsinchu, Taiwan
来源：
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2012年
关键词：
clustering algorithm; interval data; robustness; ADAPTIVE QUADRATIC DISTANCES; VALUED DATA;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we propose a robust clustering algorithm for interval data. The proposed method is based on similarity measure that is not necessary to specify a cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering algorithm. We then apply this algorithm to the real data set with cities temperature interval data. The proposed clustering algorithm actually presents its robustness.
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页数：7
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