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A review of robust clustering methods

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Abstract

Deviations from theoretical assumptions together with the presence of certain amount of outlying observations are common in many practical statistical applications. This is also the case when applying Cluster Analysis methods, where those troubles could lead to unsatisfactory clustering results. Robust Clustering methods are aimed at avoiding these unsatisfactory results. Moreover, there exist certain connections between robust procedures and Cluster Analysis that make Robust Clustering an appealing unifying framework. A review of different robust clustering approaches in the literature is presented. Special attention is paid to methods based on trimming which try to discard most outlying data when carrying out the clustering process.

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Authors and Affiliations

  1. Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, Universidad de Valladolid, Prado de la Magdalena, 47002, Valladolid, Spain

    Luis Angel García-Escudero, Alfonso Gordaliza, Carlos Matrán & Agustín Mayo-Iscar

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  1. Luis Angel García-Escudero
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  2. Alfonso Gordaliza
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  3. Carlos Matrán
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  4. Agustín Mayo-Iscar
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Correspondence to Luis Angel García-Escudero.

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García-Escudero, L.A., Gordaliza, A., Matrán, C. et al. A review of robust clustering methods. Adv Data Anal Classif 4, 89–109 (2010). https://doi.org/10.1007/s11634-010-0064-5

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  • DOI: https://doi.org/10.1007/s11634-010-0064-5

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