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Discovering Spatially Contiguous Clusters in Multivariate Geostatistical Data Through Spectral Clustering

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Advanced Data Mining and Applications (ADMA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10086))

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Abstract

Spectral clustering has recently become one of the most popular modern clustering algorithms for traditional data. However, the application of this clustering method on geostatistical data produces spatially scattered clusters, which is undesirable for many geoscience applications. In this work, we develop a spectral clustering method aimed to discover spatially contiguous and meaningful clusters in multivariate geostatistical data, in which spatial dependence plays an important role. The proposed spectral clustering method relies on a similarity measure built from a non-parametric kernel estimator of the multivariate spatial dependence structure of the data, emphasizing the spatial correlation among data locations. The capability of the proposed spectral clustering method to provide spatially contiguous and meaningful clusters is illustrated using the European Geological Surveys Geochemical database.

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Author information

Authors and Affiliations

  1. CSIRO Mineral Resources, Perth, Western Australia, Australia

    Francky Fouedjio

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  1. Francky Fouedjio
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Correspondence to Francky Fouedjio .

Editor information

Editors and Affiliations

  1. University of Technology , Sydney, New South Wales, Australia

    Jinyan Li

  2. University of Queensland , Brisbane, Australia

    Xue Li

  3. Beijing Institute of Technology , Beijing, China

    Shuliang Wang

  4. University of Western Australia , Crawley, West Australia, Australia

    Jianxin Li

  5. University of Adelaide , Adelaide, South Australia, Australia

    Quan Z. Sheng

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Fouedjio, F. (2016). Discovering Spatially Contiguous Clusters in Multivariate Geostatistical Data Through Spectral Clustering. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_38

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  • DOI: https://doi.org/10.1007/978-3-319-49586-6_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49585-9

  • Online ISBN: 978-3-319-49586-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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