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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
15/08/2016 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
BONES, C. C.; ROMANI, L. A. S.; SOUSA, E. P. M. de. |
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Afiliação: |
CHRISTIAN C. BONES, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, ICMC/USP. |
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Título: |
Improving multivariate data streams clustering. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Procedia Computer Science, v. 80, p. 461-471, 2016. |
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DOI: |
10.1016/j.procs.2016.05.325 |
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Idioma: |
Inglês |
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Notas: |
Edição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016. |
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Conteúdo: |
Clustering 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. |
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Palavras-Chave: |
Agrupamento de dados; Clusterização de dados; Data mining; Data streams; Dimensão fractal; Mineração de dados. |
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Thesaurus Nal: |
Cluster analysis; Fractal dimensions. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/146412/1/AP-improving-Bones-etal.pdf
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Marc: |
LEADER 01817nam a2200253 a 4500
001 2050917
005 2020-01-21
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1016/j.procs.2016.05.325$2DOI
100 1 $aBONES, C. C.
245 $aImproving multivariate data streams clustering.$h[electronic resource]
260 $aProcedia Computer Science, v. 80, p. 461-471, 2016.$c2016
500 $aEdição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016.
520 $aClustering 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.
650 $aCluster analysis
650 $aFractal dimensions
653 $aAgrupamento de dados
653 $aClusterização de dados
653 $aData mining
653 $aData streams
653 $aDimensão fractal
653 $aMineração de dados
700 1 $aROMANI, L. A. S.
700 1 $aSOUSA, E. P. M. de
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Embrapa Agricultura Digital (CNPTIA) |
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