Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Robust Clustering of Large Geo-referenced Data Sets
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
STING+: An Approach to Active Spatial Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Proximity and density information modeling of 2D point-data by Delaunay Diagrams has delivered a powerful exploratory and argument-free clustering algorithm [6] for geographical data mining [13]. The algorithm obtains cluster boundaries using a Short-Long criterion and detects non-convex clusters, high and low density clusters, clusters inside clusters and many other robust results. Moreover, its computation is linear in the size of the graph used. This paper demonstrates that the criterion remains effective for exploratory analysis and spatial data mining where other proximity graphs are used. It also establishes a hierarchy of the modeling power of several proximity graphs and presents how the argument free characteristic of the original algorithm can be traded for argument tuning. This enables higher than 2 dimensions by using linear size proximity graphs like k-nearest neighbors.