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dc.creatorDe-La-Hoz-Franco, Emiro
dc.creatorOviedo Carrascal, Ana Isabel
dc.creatorAriza Colpas, Paola Patricia
dc.date.accessioned2020-11-24T16:26:09Z
dc.date.available2020-11-24T16:26:09Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/11323/7459
dc.description.abstractThis article shows the use of different techniques for the extraction of information through text mining. Through this implementation, the performance of each of the techniques in the dataset analysis process can be identified, which allows the reader to recommend the most appropriate technique for the processing of this type of data. This article shows the implementation of the K-means algorithm to determine the location of the news described in RSS format and the results of this type of grouping through a descriptive analysis of the resulting clusters.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceInternational Conference on Data Mining and Big Dataspa
dc.subjectRSS news’s formatspa
dc.subjectSimple K-meansspa
dc.subjectBag of wordsspa
dc.subjectStopwordsspa
dc.subjectText miningspa
dc.titleUsing K-Means Algorithm for Description Analysis of Text in RSS News Formatspa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://link.springer.com/chapter/10.1007%2F978-981-32-9563-6_17spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttp://doi.org/10.1007/978-981-32-9563-6_17


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