Geographic data mining and knowledge discovery: An overview

H. J. Miller and J. Han. Geographic Data Mining and Knowledge Discovery, chapter Geographic data mining and knowledge discovery: An overview, pages 3–32. Taylor and Francis, London, 2001.


This book introduction presents an overview of the methods for kwoweldge discovery on geographical systems. It ferst present an overview of the techniques from the pre-processing with data enrichment  to data-reduction and projection and finally data mining.

The data mining techniques are divided into segmentation (clustering and classification), dependence analysis, deviation and outlier analysis, trend detection and generalization. For each phase some example and relative literature is presented.

Spatial Clustering can be based on a combinations of non-spatial attributes, spatial proximity, time, ad spatial attributes. Research on the computer science side focused on several salable algorithms for clustering very large spatial datasets and methods for finding proximity relationships between clusters and spatial features (Knorr and NG, 1996). 

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