dc.creator | Carrizosa Priego, Emilio José | es |
dc.creator | Kurishchenko, Kseniia | es |
dc.creator | Marín, Alfredo | es |
dc.creator | Romero Morales, María Dolores | es |
dc.date.accessioned | 2022-07-04T09:44:01Z | |
dc.date.available | 2022-07-04T09:44:01Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Carrizosa Priego, E.J., Kurishchenko, K., Marín, A. y Romero Morales, M.D. (2021). Interpreting clusters via prototype optimization. Omega, 107, 2-13. | |
dc.identifier.issn | 0305-0483 | es |
dc.identifier.uri | https://hdl.handle.net/11441/134950 | |
dc.description.abstract | In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analy sis. Our goal is to find an explanation for each cluster, such that clusters are characterized as precisely
and distinctively as possible, i.e., the explanation is fulfilled by as many as possible individuals of the
corresponding cluster, true positive cases, and by as few as possible individuals in the remaining clus ters, false positive cases. We assume that a dissimilarity between the individuals is given, and propose
distance-based explanations, namely those defined by individuals that are close to its so-called proto type. To find the set of prototypes, we address the biobjective optimization problem that maximizes the
total number of true positive cases across all clusters and minimizes the total number of false positive
cases, while controlling the true positive rate as well as the false positive rate in each cluster. We develop
two mathematical optimization models, inspired by classic Location Analysis problems, that differ in the
way individuals are allocated to prototypes. We illustrate the explanations provided by these models and
their accuracy in both real-life data as well as simulated data. | es |
dc.format | application/pdf | es |
dc.format.extent | 13 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Omega, 107, 2-13. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine Learning | es |
dc.subject | Interpretability | es |
dc.subject | Cluster Analysis | es |
dc.subject | Prototypes | es |
dc.subject | Mixed-Integer Programming | es |
dc.title | Interpreting clusters via prototype optimization | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa | es |
dc.relation.publisherversion | https://doi.org/10.1016/j.omega.2021.102543 | es |
dc.identifier.doi | 10.1016/j.omega.2021.102543 | es |
dc.journaltitle | Omega | es |
dc.publication.volumen | 107 | es |
dc.publication.initialPage | 2 | es |
dc.publication.endPage | 13 | es |