Título:	Improving multivariate data streams clustering.
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.
Ano de publicação:	2016
Referência:	Procedia Computer Science, v. 80, p. 461-471, 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.
NAL Thesaurus:	Cluster analysis Fractal dimensions
Palavras-chave:	Mineração de dados Dimensão fractal Clusterização de dados Agrupamento de dados Data mining Data streams
Digital Object Identifier:	10.1016/j.procs.2016.05.325
Notas:	Edição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016.
Tipo do material:	Artigo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Artigo em anais de congresso (CNPTIA)