%0 Journal Article %1 springerlink:10.1007/s11634-010-0064-5 %A García-Escudero, Luis %A Gordaliza, Alfonso %A Matrán, Carlos %A Mayo-Iscar, Agustín %C Berlin / Heidelberg %D 2010 %I Springer %J Advances in Data Analysis and Classification %K cluster clustering review robust %P 89-109 %R 10.1007/s11634-010-0064-5 %T A review of robust clustering methods %U http://dx.doi.org/10.1007/s11634-010-0064-5 %V 4 %X 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.

@article{springerlink:10.1007/s11634-010-0064-5, 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.}, added-at = {2011-03-07T23:25:55.000+0100}, address = {Berlin / Heidelberg}, affiliation = {Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, Universidad de Valladolid, Prado de la Magdalena, 47002 Valladolid, Spain}, author = {García-Escudero, Luis and Gordaliza, Alfonso and Matrán, Carlos and Mayo-Iscar, Agustín}, biburl = {https://www.bibsonomy.org/bibtex/26bbebb767aba1a9ccc1a4e6809459f43/vivion}, description = {SpringerLink - Advances in Data Analysis and Classification, Volume 4, Numbers 2-3}, doi = {10.1007/s11634-010-0064-5}, interhash = {b5f58d7a4dbde4f946d1a5219e44c075}, intrahash = {6bbebb767aba1a9ccc1a4e6809459f43}, issn = {1862-5347}, issue = {2}, journal = {Advances in Data Analysis and Classification}, keyword = {Mathematics}, keywords = {cluster clustering review robust}, pages = {89-109}, publisher = {Springer}, timestamp = {2011-03-07T23:25:55.000+0100}, title = {A review of robust clustering methods}, url = {http://dx.doi.org/10.1007/s11634-010-0064-5}, volume = 4, year = 2010 }