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dc.contributor.authorTORTI FRANCESCAen_GB
dc.contributor.authorPERROTTA DOMENICOen_GB
dc.contributor.authorRIANI MARCOen_GB
dc.contributor.authorCERIOLI ANDREAen_GB
dc.date.accessioned2020-01-30T01:05:00Z-
dc.date.available2018-12-13en_GB
dc.date.available2020-01-30T01:05:00Z-
dc.date.created2018-12-05en_GB
dc.date.issued2019en_GB
dc.date.submitted2016-12-06en_GB
dc.identifier.citationADVANCES IN DATA ANALYSIS AND CLASSIFICATION vol. 13 p. 227-257en_GB
dc.identifier.issn1862-5347 (online)en_GB
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs11634-018-0331-4en_GB
dc.identifier.urihttps://publications.jrc.ec.europa.eu/repository/handle/JRC104609-
dc.description.abstractWe assess the performance of state-of-the-art robust clustering tools for regression structures under a variety of different data configurations. We focus on two methodologies that use trimming and restrictions on group scatters as their main ingredients. We also give particular care to the data generation process through the development of a flexible simulation tool for mixtures of regressions, where the user can control the degree of overlap between the groups. Level of trimming and restriction factors are input parameters for which appropriate tuning is required. Since we find that incorrect specification of the second-level trimming in the Trimmed CLUSTering REGression model (TCLUST-REG) can deteriorate the performance of the method, we propose an improvement where the second-level trimming is not fixed in advance but is data dependent.We then compare our adaptive version of TCLUST-REG with the Trimmed Cluster Weighted Restricted Model (TCWRM) which provides a powerful extension of the robust clusterwise regression methodology. Our overall conclusion is that the two methods perform comparably, but with notable differences due to the inherent degree of modeling implied by them.en_GB
dc.description.sponsorshipJRC.I.3-Text and Data Miningen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherSPRINGER HEIDELBERGen_GB
dc.relation.ispartofseriesJRC104609en_GB
dc.titleAssessing trimming methodologies for clustering linear regression dataen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1007/s11634-018-0331-4 (online)en_GB
JRC Directorate:Joint Research Centre Corporate Activities

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