Spatial Data Mining
Spatial Data Mining is a subfield of data mining that specifically focuses on discovering previously unknown, spatially relevant patterns, relationships, or knowledge from large spatial datasets by incorporating spatial topology, proximity, and dependency as fundamental differentiating constraints.
2.1K
Publications
137.6K
Citations
5.3K
Authors
1.6K
Institutions
Publications per year
2017–2026
753
2.1K
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester, Hans‐Peter Kriegel, Jörg Sander et al. · 1996 · 19.1K citations
Erich Schubert, Jörg Sander, Martin Ester et al. · ACM Transactions on Database Systems · 2017 · 2.6K citations
Efficient and Effective Clustering Methods for Spatial Data Mining
Raymond T. Ng, Jiawei Han · 1994 · 1.8K citations
Yu Zheng · ACM Transactions on Intelligent Systems and Technology · 2015 · 1.6K citations
Computing, Data Science, Data Mining +15
1–5 of 2.1K
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1–5 of 5.3K
1.6K

University of Minnesota
Minneapolis, United States
26 h-index
124 publications
9.5K citations

Chinese Academy of Sciences
Beijing, China
25 h-index
134 publications
6.3K citations
Wuhan University
Wuhan, China
22 h-index
139 publications
4.6K citations

Peking University
Beijing, China
20 h-index
73 publications
2.6K citations
Yunnan University
Kunming, China
20 h-index
87 publications
3K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
19 h-index
46 publications
3K citations

The University of Queensland
Brisbane, Australia
19 h-index
57 publications
3.9K citations

Zhejiang University
Hangzhou, China
19 h-index
69 publications
3.6K citations
1–8 of 1.6K
TKDE · 48 publications · 5.9K citations
33
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66 publications · 3.8K citations
32
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35 publications · 1.5K citations
22
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IJGI · 43 publications · 1.5K citations
21
h-index
27 publications · 1.9K citations
20
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