A fast algorithm for robust constrained clustering

Heinrich Fritz, Luis A. García-Escudero and Agustín Mayo-Iscar

Computational Statistics & Data Analysis, 2013, vol. 61, issue C, 124-136

Abstract: The application of “concentration” steps is the main principle behind Forgy’s k-means algorithm and the fast-MCD algorithm. Despite this coincidence, it is not completely straightforward to combine both algorithms for developing a clustering method which is not severely affected by few outlying observations and being able to cope with non spherical clusters. A sensible way of combining them relies on controlling the relative cluster scatters through constrained concentration steps. With this idea in mind, a new algorithm for the TCLUST robust clustering procedure is proposed which implements such constrained concentration steps in a computationally efficient fashion.

Keywords: Cluster analysis; Robustness; Impartial trimming; Classification EM algorithm; TCLUST (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:61:y:2013:i:c:p:124-136

DOI: 10.1016/j.csda.2012.11.018

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