2008/05/26 by Luis A. García-Escudero, Alfonso Gordaliza, Carlos Matrán +1 · 3 citations
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Survey Sampling and Estimation Techniques #math.ST #msc:62H3 #stat.TH
paper · pdf · doi:10.1214/07-aos515
published as Annals of Statistics 2008, Vol. 36, No. 3, 1324-1345 · Published in at http://dx.doi.org/10.1214/07-AOS515 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2008/05/26 · arxiv created 2008/06/18 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01
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.