Abstract
Multivariate georeferenced data have become omnipresent in the many scientific fields and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in terms of a concept of dissimilarity. In this work, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the multivariate spatial dependence structure of data. It integrates existing methods to find the optimal cluster number. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is illustrated to the National Geochemical Survey of Australia data.
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© 2016 Springer International Publishing Switzerland
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Fouedjio, F. (2016). A Clustering Approach for Discovering Intrinsic Clusters in Multivariate Geostatistical Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_39
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DOI: https://doi.org/10.1007/978-3-319-41920-6_39
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