A fuzzy approach to robust regression clustering

被引：6
|
作者：
Dotto, Francesco [1 ]
Farcomeni, Alessio [2 ]
Angel Garcia-Escudero, Luis [3 ]
Mayo-Iscar, Agustin [4 ]
机构：
[1] Univ Roma La Sapienza, Dipartimento Sci Stat, Piazzale Aldo Moro 5, I-00185 Rome, Italy
[2] Univ Roma La Sapienza, Dipartimento Sanita Pubbl & Malattie Infett, Piazzale Aldo Moro 5, I-00185 Rome, Italy
[3] Univ Valladolid, Dept Estadist & Invest Operat, Paseo Belen 7, E-47011 Valladolid, Spain
[4] Univ Valladolid, Dept Estadist & Invest Operat, Calle Ramon Y Cajal 7, E-47005 Valladolid, Spain
来源：
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION | 2017年 / 11卷 / 04期
关键词：
Robustness; Fuzzy clustering; Trimming; Regression clustering; ALGORITHM;
D O I：
10.1007/s11634-016-0271-9
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
A new robust fuzzy regression clustering method is proposed. We estimate coefficients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a fixed proportion of observations. Assignments to clusters are fuzzy: observations contribute to estimates in more than one single cluster. We describe general criteria for tuning the method. The proposed method seems to be robust with respect to different types of contamination.
引用
收藏
页码：691 / 710
页数：20
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