A fast algorithm for robust constrained clustering

Authors:
Heinrich Fritz;Luis A. GarcíA-Escudero;AgustíN Mayo-Iscar
Affiliations:
Department of Statistics and Probability Theory, Vienna University of Technology, Austria;Department of Statistics and Operations Research and IMUVA, University of Valladolid, Spain;Department of Statistics and Operations Research and IMUVA, University of Valladolid, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2013

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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.