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Author(s): Joaquim F. Pinto da Costa
Isabel Silva
M. Eduarda Silva
Title: Time dependent clustering of time series
Issue Date: 2007
Abstract: In this work we consider the problem of clustering time series. Contrary to other works on this topic, our main concern is to let the most important observations, for instance the most recent, have a larger weight on the analysis. This is done by defining similarities measures between two time series, based on Pearson's correlation coefficient, which uses the notion of weighted mean and weighted covariance, where the weights increase monotonically with the time. We use these measures, which are metrics between time series, as a similarity or dissimilarity index between the $n$ time series to be clustered. We apply a very well known partitional method, the K-means, with some adaptations to make it able to choose the number of clusters.
Subject: Estatística, Matemática
Statistics, Mathematics
Scientific areas: Ciências exactas e naturais::Matemática
Natural sciences::Mathematics
Source: Proceedings of the 56th Session of the ISI
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
Appears in Collections:FCUP - Artigo em Livro de Atas de Conferência Internacional
FEP - Artigo em Livro de Atas de Conferência Internacional
FEUP - Artigo em Livro de Atas de Conferência Internacional

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