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Abstract: Spatial data mining is a process to discover interesting, potentially useful and high utility patterns embedded in spatial databases. Efficient tools for extracting information from spatial data sets can be of importance to organizations which own, generate and manage large spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after "materializing" spatial relationships. However, the key property of spatial data is that of... (Update)
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...auto correlation. Spatial auto correlation is the property that spatially referenced objects influence other objects in the neighborhood [7]. This property is often referred to as the first law of geography: everything is related to everything else but nearby things are more...
...financial support of the Swiss OFES. have been used in a wide range of applications: character recognition ( 14] geospatial data mining ([4]) affine invariant texture classification ( 6] time series classification ( 10] and face detection and recognition ( 15] 9] 7]...
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BibTeX entry: (Update)
S. Chawla, S. Shekhar, W-L Wu, and U. Ozesmi. Modeling spatial dependencies for mining geospatial data: An introduction. In Geographic data mining and Knowledge Discovery(GKD) (Ed. Harvey Miller and Jiawei Han), under contract with Taylor and Francis, URL: http://www.geog.utah.edu/ ~hmiller/gkd text. http://citeseer.ist.psu.edu/chawla01modeling.html More
@misc{ chawla-modeling,
author = "S. Chawla and S. Shekhar and W. Wu and U. Ozesmi",
title = "Modeling spatial dependencies for mining geospatial data: An introduction",
text = "Modeling spatial dependencies
for mining geospatial data: An introduction. In Geographic data mining and
Knowledge Discovery(GKD) (Ed. Harvey Miller and Jiawei Han), under contract
with Taylor and Francis, URL: http://www.geog.utah.edu/ ~hmiller/gkd text.",
url = "citeseer.ist.psu.edu/chawla01modeling.html" }
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Documents on the same site (http://www.cs.umn.edu/research/shashi-group/GKD-DM/):
Modeling Spatial Dependencies for Mining Geospatial Data: An.. - Chawla, al. (2000) (Correct)
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