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Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery ArchiMer
Grassi, Kelly; Poisson-caillault, Emilie; Bigand, André; Lefebvre, Alain.
In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which contribute to explain the structure and the function of the ecosystem. To identify such states, many scientific consortiums promote the implementation of Integrated Observing Systems which generate increasing amount of complex multivariate/multisource/multiscale datasets. Extracting the most relevant ecological information from such complex datasets requires the implementation of Machine Learning-based processing tools. In this context, we...
Tipo: Text Palavras-chave: Clustering; Pattern discovery; Time series; Multi-Level Spectral Clustering; English Channel.
Ano: 2020 URL:
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