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. A popular approach is to apply classical data mining techniques after transforming spatial components into non-spatial components via feature selection. An alternative is to explore new models, new objective functions, and new patterns which are more suitable for spatial data and their unique properties. This chapter investigates techniques in the literature to incorporate spatial components via feature selection, new models, new objective functions, and new patterns.
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Shekhar, S., Huang, Y., Wu, W., Lu, C.T., Chawla, S. (2001). What’s Spatial About Spatial Data Mining: Three Case Studies. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_26
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DOI: https://doi.org/10.1007/978-1-4615-1733-7_26
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