A Bayesian Approach for the Clustering of Short Time Series - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle Year : 2006

A Bayesian Approach for the Clustering of Short Time Series

Abstract

Microarrays allow monitoring of thousands of genes over time periods. However, due to the low number of time points of the gene expression series, taking the temporal dependences into account when clustering the data is an hard task. Moreover, classes very interesting for the biologist, but sparse with regard to all the other genes, can be completely omitted by the standard approaches. We propose a Bayesian approach for this problem. A mixture model is used to describe and classify the data. The parameters of this model are constrained by a prior distribution defined with a new type of model that expresses our prior knowledge. These knowledge allow to take the temporal dependences into account in natural way, as well as to express rough temporal profiles about classes of interest.
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Dates and versions

lirmm-00113350 , version 1 (13-11-2006)

Identifiers

  • HAL Id : lirmm-00113350 , version 1

Cite

Laurent Brehelin. A Bayesian Approach for the Clustering of Short Time Series. Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, 2006, 20, pp.697-716. ⟨lirmm-00113350⟩
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