A fuzzy approach to robust regression clustering

Francesco Dotto (), Alessio Farcomeni, Luis Angel García-Escudero () and Agustín Mayo-Iscar ()
Additional contact information
Francesco Dotto: Università di Roma “La Sapienza”
Luis Angel García-Escudero: Universidad de Valladolid
Agustín Mayo-Iscar: Universidad de Valladolid

Advances in Data Analysis and Classification, 2017, vol. 11, issue 4, No 3, 710 pages

Abstract: Abstract 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.

Keywords: Robustness; Fuzzy clustering; Trimming; Regression clustering; 62H30 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11634-016-0271-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:11:y:2017:i:4:d:10.1007_s11634-016-0271-9

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-016-0271-9

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().