Abstract
Spatial Data Mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. This chapter provides an overview on the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research.
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Shekhar, S., Zhang, P., Huang, Y. (2005). Spatial Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_39
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DOI: https://doi.org/10.1007/0-387-25465-X_39
Publisher Name: Springer, Boston, MA
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