Data/hora: 03/06/2024 15:41:08

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	05/01/2016	
Data da última atualização: 	21/01/2020	
Autoria: 	BONES, C. C.; ROMANI, L. A. S.; SOUSA, E. P. M. de.	
Afiliação: 	CHRISTIAN C. BONES, USP, São Carlos, SP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, USP, São Carlos, SP.	
Título: 	Clustering multivariate climate data streams using fractal dimension.	
Ano de publicação: 	2015	
Fonte/Imprenta: 	In: BRAZILIAN SYMPOSIUM ON DATABASES, 30., 2015, Petrópolis. Proceedings... Petrópolis: Laboratório Nacional de Computação Científica, 2015.	
Páginas: 	p. 41-52.	
Idioma: 	Inglês	
Notas: 	SBBD 2015.	
Conteúdo: 	Abstract. A data stream is a flow of data produced continuously along the time. Storing and analyzing such information become challenging due to exponential growth of the data volume collected. In this context, some methods were proposed to cluster data streams with similar behavior along the time. However, those methods have failed on clustering data flows with more than one attribute, i.e., multivariate flows. This paper introduces a new method to cluster multivariate data streams, based on fractal dimension, reading the data only once. We evaluated our method over real multivariate data streams generated by climate sensors. Not only was our method able to cluster the flows of data, but also identified sensors with similar behavior during the analyzed period.	
Palavras-Chave: 	Análise multivariada; Banco de dados; Dimensão fractal.	
Thesagro: 	Clima.	
Thesaurus Nal: 	Cluster analysis; Databases; Fractal dimensions; Multivariate analysis.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (18577-1) - UPC - AA - DD -  -  -  - DIS