Abstract
We explore the application of Spatial Data Mining, the partially automated search for hidden patterns in georeferenced databases, to the analysis of ecological data. A version of the subgroup mining algorithm is described that searches for deviation patterns directly in a spatial database, automatically incorporating spatial information stored in a GIS into the hypothesis space of a data mining search. We discuss results obtained on a multi-relational biodiversity data set recorded in Niger. Vegetation records are georeferenced and associated with a set of environmental parameters. Further data provide information on climate, soil conditions, and location of spatial objects like rivers, streets and cities. The subgroup mining finds dependencies of a plant species on other species, on local parameters and non-local environmental parameters.
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May, M., Ragia, L. (2002). Spatial Subgroup Discovery Applied to the Analysis of Vegetation Data. In: Karagiannis, D., Reimer, U. (eds) Practical Aspects of Knowledge Management. PAKM 2002. Lecture Notes in Computer Science(), vol 2569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36277-0_6
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DOI: https://doi.org/10.1007/3-540-36277-0_6
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