CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Approaches for scaling DBSCAN algorithm to large spatial databases
Journal of Computer Science and Technology
Self-Organizing Maps
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Clustering spatial data with a hybrid EM approach
Pattern Analysis & Applications
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Computers & Geosciences
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The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, which is based on Self-Organizing Maps. This paper describes this tool, and shows that it is adequate for exploring spatial data.