Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1050917
Research center of Embrapa/Collection: Embrapa Informática Agropecuária - Artigo em anais de congresso (ALICE)
Date Issued: 2016
Type of Material: Artigo em anais de congresso (ALICE)
Authors: BONES, C. C.
ROMANI, L. A. S.
SOUSA, E. P. M. de
Additional Information: CHRISTIAN C. BONES, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ELAINE P. M. DE SOUSA, ICMC/USP.
Title: Improving multivariate data streams clustering.
Publisher: Procedia Computer Science, v. 80, p. 461-471, 2016.
Language: en
Notes: Edição dos Proceedings do 16th International Conference on Computational Science, San Diego, 2016.
Keywords: Mineração de dados
Dimensão fractal
Clusterização de dados
Agrupamento de dados
Data mining
Data streams
Description: 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
Data Created: 2016-08-15
Appears in Collections:Artigo em anais de congresso (CNPTIA)

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