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CABRA: Clustering algorithm based on regular arrangement
dc.contributor.author | C-Rella, Jorge | |
dc.date.accessioned | 2024-07-15T14:20:05Z | |
dc.date.available | 2024-07-15T14:20:05Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Jorge, C. (2024). CABRA: Clustering algorithm based on regular arrangement. Operations Research Letters, 107137. https://doi.org/10.1016/j.orl.2024.107137 | es_ES |
dc.identifier.issn | 0167-6377 (print) | |
dc.identifier.issn | 1872-7468 (electronic) | |
dc.identifier.uri | http://hdl.handle.net/2183/38006 | |
dc.description.abstract | [Abstract]: Clustering is an unsupervised learning technique for organizing complex datasets into coherent groups. A novel clustering algorithm is presented, with a simple grouping concept depending on only one hyperparameter, which makes it suitable for further extensions to any topology and space. It is compared to state-of-the-art algorithms, overall achieving a better performance independently on the structure and complexity of the data, making the proposed algorithm a valuable tool for real applications such as market segmentation, sentiment analysis and anomaly detection. | es_ES |
dc.description.sponsorship | This research has been financed by the Grant PID2020-113578RB-I00, funded by MCIN/AEI/10.2039/501100011033/. It has also been supported by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020/14) and by CITIC that is supported by Xunta de Galicia, convenio de colaboración entre la Consellería de Cultura, Educación, Formación Profesional e Universidades y las universidades gallegas para el refuerzo de los centros de investigación del Sistema Universitario de Galicia (CIGUS). The first author was financed by the Axencia Galega de Innovación Industrial PhD Grant 14-IN606D-2021-2607768. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020/14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; 14-IN606D-2021-2607768 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.orl.2024.107137 | es_ES |
dc.rights | Atribución 4.0 Internacional (CC-BY) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Clustering | es_ES |
dc.subject | Segmentation | es_ES |
dc.subject | Classification | es_ES |
dc.subject | Outlier detection | es_ES |
dc.title | CABRA: Clustering algorithm based on regular arrangement | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Operations Research Letters | es_ES |
UDC.volume | 55 | es_ES |
UDC.issue | 107137 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 7 | es_ES |
dc.identifier.doi | 10.1016/j.orl.2024.107137 |
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