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A reweighting approach to robust clustering |
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| Authors: | Francesco Dotto Alessio Farcomeni Luis Angel García-Escudero Agustín Mayo-Iscar |
| Institution: | 1.Dipartimento di Scienze Statistiche,Università di Roma “La Sapienza”,Rome,Italy;2.Dipartimento di Sanità Pubblica e Malattie Infettive,Università di Roma “La Sapienza”,Rome,Italy;3.Departamento de Estadística e Investigación Operativa,Universidad de Valladolid,Valladolid,Spain;4.Departamento de Estadística e Investigación Operativa,Universidad de Valladolid,Valladolid,Spain |
| Abstract: | An iteratively reweighted approach for robust clustering is presented in this work. The method is initialized with a very robust clustering partition based on an high trimming level. The initial partition is then refined to reduce the number of wrongly discarded observations and substantially increase efficiency. Simulation studies and real data examples indicate that the final clustering solution has both good properties in terms of robustness and efficiency and naturally adapts to the true underlying contamination level. |
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