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Interpretable market segmentation on high dimension data
dc.contributor.author | Eiras-Franco, Carlos | |
dc.contributor.author | Guijarro-Berdiñas, Bertha | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.contributor.author | Bahamonde, Antonio | |
dc.date.accessioned | 2018-10-05T17:09:27Z | |
dc.date.available | 2018-10-05T17:09:27Z | |
dc.date.issued | 2018-09-17 | |
dc.identifier.citation | Eiras-Franco, C.; Guijarro-Berdiñas, B.; Alonso-Betanzos, A.; Bahamonde, A. Interpretable Market Segmentation on High Dimension Data. Proceedings 2018, 2, 1171. | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://hdl.handle.net/2183/21119 | |
dc.description | Trátase dun resumo estendido da ponencia | |
dc.description.abstract | [Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, the interpretability of machine learning algorithms is becoming increasingly relevant and even becoming a legal requirement, all of which increases the demand for such algorithms. In this work we propose a quality measure that factors in the interpretability of results. Additionally, we present a grouping algorithm on dyadic data that returns results with a level of interpretability selected by the user and capable of handling large volumes of data. Experiments show the accuracy of the results, on par with traditional methods, as well as its scalability. | es_ES |
dc.description.sponsorship | Ministerio de Economía y Competitividad; TIN 2015-65069-C2 | es_ES |
dc.description.sponsorship | Red Española de Big Data y Análisis de datos escalable; TIN2016-82013-REDT | es_ES |
dc.description.sponsorship | Xunta de Galicia; GRC2014/035 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | M D P I AG | es_ES |
dc.relation.uri | https://doi.org/10.3390/proceedings2181171 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Market segmentation | es_ES |
dc.subject | Interpretability | es_ES |
dc.subject | Explainability | es_ES |
dc.subject | Scalability | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Big Data | es_ES |
dc.title | Interpretable market segmentation on high dimension data | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 18 | es_ES |
UDC.issue | 2 | es_ES |
UDC.startPage | 1171 | es_ES |
dc.identifier.doi | 10.3390/proceedings2181171 | |
UDC.conferenceTitle | Proceedings XoveTIC Conference 2018 | es_ES |