Unsupervised learning algorithms applied to grouping problems

amelec, viloria; Lizardo Zelaya, Nelson Alberto; Varela, Noel

doi:https://doi.org/10.1016/j.procs.2020.07.099

dc.contributor.author	amelec, viloria	spa
dc.contributor.author	Lizardo Zelaya, Nelson Alberto	spa
dc.contributor.author	Varela, Noel	spa
dc.date.accessioned	2021-01-04T21:16:37Z
dc.date.available	2021-01-04T21:16:37Z
dc.date.issued	2020
dc.identifier.issn	1877-0509	spa
dc.identifier.uri	https://hdl.handle.net/11323/7654	spa
dc.description.abstract	One of the tasks of great interest within process mining is the discovery of business process models, which consists of using an event log as input and producing a business process model by analyzing the data contained in the log and applying a process mining method, task and/or technique. The discovery allows the identification of the behaviors contained in the cases of the event log in order to detect possible deviations and/or validate that the business process is executed according to the business requirements. This paper presents an approach based on unsupervised learning techniques for the grouping of traces to generate simpler and more understandable models. The algorithms implemented for clustering are K-means, hierarchical agglomerative and density-based spatial clustering of applications with noise (DBSCAN).	spa
dc.format.mimetype	application/pdf	spa
dc.language.iso	eng
dc.publisher	Corporación Universidad de la Costa	spa
dc.rights	CC0 1.0 Universal	spa
dc.rights.uri	http://creativecommons.org/publicdomain/zero/1.0/	spa
dc.source	Procedia Computer Science	spa
dc.subject	Trace grouping	spa
dc.subject	Data mining	spa
dc.subject	Unsupervised learning techniques	spa
dc.title	Unsupervised learning algorithms applied to grouping problems	spa
dc.type	Artículo de revista	spa
dc.source.url	https://www.sciencedirect.com/science/article/pii/S1877050920317993	spa
dc.rights.accessrights	info:eu-repo/semantics/openAccess	spa
dc.identifier.doi	https://doi.org/10.1016/j.procs.2020.07.099	spa
dc.identifier.instname	Corporación Universidad de la Costa	spa
dc.identifier.reponame	REDICUC - Repositorio CUC	spa
dc.identifier.repourl	https://repositorio.cuc.edu.co/	spa
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