Annals of Statistics

A general trimming approach to robust cluster Analysis

Luis A. García-Escudero, Alfonso Gordaliza, Carlos Matrán, and Agustin Mayo-Iscar

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Abstract

We introduce a new method for performing clustering with the aim of fitting clusters with different scatters and weights. It is designed by allowing to handle a proportion α of contaminating data to guarantee the robustness of the method. As a characteristic feature, restrictions on the ratio between the maximum and the minimum eigenvalues of the groups scatter matrices are introduced. This makes the problem to be well defined and guarantees the consistency of the sample solutions to the population ones.

The method covers a wide range of clustering approaches depending on the strength of the chosen restrictions. Our proposal includes an algorithm for approximately solving the sample problem.

Article information

Source
Ann. Statist., Volume 36, Number 3 (2008), 1324-1345.

Dates
First available in Project Euclid: 26 May 2008

Permanent link to this document
https://projecteuclid.org/euclid.aos/1211819566

Digital Object Identifier
doi:10.1214/07-AOS515

Mathematical Reviews number (MathSciNet)
MR2418659

Zentralblatt MATH identifier
1360.62328

Subjects
Primary: 62H3
Secondary: 62H3

Keywords
Robustness cluster analysis trimming asymptotics trimmed k-means EM-algorithm fast-MCD algorithm Dykstra’s algorithm

Citation

García-Escudero, Luis A.; Gordaliza, Alfonso; Matrán, Carlos; Mayo-Iscar, Agustin. A general trimming approach to robust cluster Analysis. Ann. Statist. 36 (2008), no. 3, 1324--1345. doi:10.1214/07-AOS515. https://projecteuclid.org/euclid.aos/1211819566


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