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García Escudero, Luis Ángel Mayo Iscar, Agustín 2024-12-13T23:03:14Z 2024-12-13T23:03:14Z 2024 WIREs Computational Statistics, 2024, vol. 16, n. 4, e1658 1939-5108 https://uvadoc.uva.es/handle/10324/72570 10.1002/wics.1658 4 WIREs Computational Statistics 16 1939-0068 Producción Científica Clustering is one of the most widely used unsupervised learning techniques. However, it is well-known that outliers can have a significantly adverse impact on commonly applied clustering methods. On the other hand, clustered outliers can be particularly detrimental to (even robust) statistical procedures. Therefore, it makes sense to combine concepts from Robust Statistics and Cluster Analysis to deal with both clusters and outliers simultaneously through robust clustering approaches. Among the existing robust clustering techniques, we focus on those that rely on (impartial) trimming. Trimming offers the user an easy interpretation, as standard well-known clustering methods are applied after a fraction of the potentially most outlying observations is removed. This trimming approach, when combined with appropriate constraints on the clusters' dispersion parameters, has shown a good performance and can be implemented efficiently thorough available algorithms. Este trabajo forma parte del proyecto de investigación PID2021-128314NB-I00 financiado por MCIN/AEI/10.13039/501100011033/FEDER. application/pdf eng Wiley info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ © 2024 The Author(s) Atribución 4.0 Internacional clustering model-based clustering robustness trimming 1209 Estadística Robust clustering based on trimming info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://wires.onlinelibrary.wiley.com/doi/10.1002/wics.1658 SI