Data/hora: 14/05/2024 07:20:23

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 15/08/2016
Data da última atualização: | 21/01/2020
Autoria: | BONES, C. C.; ROMANI, L. A. S.; SOUSA, E. P. M. de.
Afiliação: | CHRISTIAN C. BONES, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, ICMC/USP.
Título: | Improving multivariate data streams clustering.
Ano de publicação: | 2016
Fonte/Imprenta: | Procedia Computer Science, v. 80, p. 461-471, 2016.
DOI: | 10.1016/j.procs.2016.05.325
Idioma: | Inglês
Notas: | Edição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016.
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.
Palavras-Chave: | Agrupamento de dados; Clusterização de dados; Data mining; Data streams; Dimensão fractal; Mineração de dados.
Thesaurus Nal: | Cluster analysis; Fractal dimensions.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/146412/1/AP-improving-Bones-etal.pdf
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (18854-1) - UPC - AA - DD -  -  -  - DIS