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dc.contributor.authorBONES, C. C.pt_BR
dc.contributor.authorROMANI, L. A. S.pt_BR
dc.contributor.authorSOUSA, E. P. M. dept_BR
dc.date.accessioned2016-08-15T11:11:11Zpt_BR
dc.date.available2016-08-15T11:11:11Zpt_BR
dc.date.created2016-08-15pt_BR
dc.date.issued2016pt_BR
dc.identifier.citationProcedia Computer Science, v. 80, p. 461-471, 2016.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1050917pt_BR
dc.descriptionClustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivariate data streams based on their evolving behavior over time, exploring the correlations among their attributes by computing the fractal dimension. Experimental results with climate data streams show that the clusters' quality and compactness can be improved compared to the competing method, leading to the thoughtfulness that attributes correlations cannot be put aside. In fact, the clusters' compactness are 7 to 25 times better using our method. Our framework also proves to be an useful tool to assist meteorologists in understanding the climate behavior along a period of time.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectMineração de dadospt_BR
dc.subjectDimensão fractalpt_BR
dc.subjectClusterização de dadospt_BR
dc.subjectAgrupamento de dadospt_BR
dc.subjectData miningpt_BR
dc.subjectData streamspt_BR
dc.titleImproving multivariate data streams clustering.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2020-01-21T11:11:11Zpt_BR
dc.subject.nalthesaurusCluster analysispt_BR
dc.subject.nalthesaurusFractal dimensionspt_BR
dc.description.notesEdição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016.pt_BR
riaa.ainfo.id1050917pt_BR
riaa.ainfo.lastupdate2020-01-21 -02:00:00pt_BR
dc.identifier.doi10.1016/j.procs.2016.05.325pt_BR
dc.contributor.institutionCHRISTIAN C. BONES, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, ICMC/USP.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPTIA)

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