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Clustering and Visualization of Multivariate Time Series
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Author(s): Alfredo Vellido (Universidad Politécnica de Cataluña, Spain)and Iván Olier (Universidad Politécnica de Cataluña, Spain)
Copyright: 2010
Pages: 19
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 10.4018/978-1-60566-766-9.ch008
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Abstract
The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS through unsupervised clustering and visualization. In this chapter, the authors describe generative topographic mapping through time (GTM-TT), a model with foundations in probability theory that performs such tasks. The standard version of this model has several limitations that limit its applicability. Here, the authors reformulate it within a Bayesian approach using variational techniques. The resulting variational Bayesian GTM-TT, described in some detail, is shown to behave very robustly in the presence of noise in the MTS, helping to avert the problem of data overfitting.
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