Spatial Association Rule Mining
Spatial Association Rule Mining is a data mining technique that identifies statistically significant co-occurrence patterns among spatial features or objects within geographic space, distinguished by its incorporation of spatial relationships such as proximity, contiguity, and spatial autocorrelation in the rule discovery process.
84
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5.4K
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223
Authors
119
Institutions
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2017–2026
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Comparing implementations of global and local indicators of spatial association
Roger Bivand, David W. S. Wong · Test · 2018 · 1.1K citations · Full text
A partial join approach for mining co-location patterns
Jin Soung Yoo, Shashi Shekhar, John Smith et al. · 2004 · 197 citations
Mining frequent neighboring class sets in spatial databases
Yasuhiko Morimoto · 2001 · 189 citations
Jaiwei Han, Krzysztof Koperski, Nebojša Stefanović · 1997 · 179 citations · Full text
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University of Houston
Houston, United States
3 h-index
11 publications
286 citations
University of Bari Aldo Moro
Bari, Italy
3 h-index
9 publications
589 citations

University of Minnesota
Minneapolis, United States
3 h-index
8 publications
694 citations
Simon Fraser University
Burnaby, Canada
2 h-index
6 publications
648 citations
University at Buffalo, State University of New York
Buffalo, United States
2 h-index
2 publications
100 citations
Yunnan University
Kunming, China
2 h-index
6 publications
190 citations

Bureau of Economic Analysis
Washington D.C., United States
2 h-index
2 publications
44 citations
Twin Cities Orthopedics
Edina, United States
2 h-index
6 publications
300 citations
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3 publications · 205 citations
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2 publications · 110 citations
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2 publications · 73 citations
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2 publications · 30 citations
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2 publications · 152 citations
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