Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Applying UML and patterns: an introduction to object-oriented analysis and design
Applying UML and patterns: an introduction to object-oriented analysis and design
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
Two-Dimensional Voronoi Diagrams in the Lp-Metric
Journal of the ACM (JACM)
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Delauney Triangulation Closely Approximates the Complete Euclidean Graph
WADS '89 Proceedings of the Workshop on Algorithms and Data Structures
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
Region-restricted clustering for geographic data mining
Computational Geometry: Theory and Applications
A hybrid EM approach to spatial clustering
Computational Statistics & Data Analysis
A Complex Networks Approach to Demographic Zonification
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Geospatial knowledge discovery framework for crime domain
Transactions on computational science XIII
Spatial clustering of structured objects
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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In many GIS settings, the Euclidean metric is not applicable as the model for distance between points. Other geometric models are needed in many practical scenarios, for which urban geography is a common example. Recently, Estivill-Castro and Lee [8] proposed an effective and efficient boundary-based clustering method overcoming drawbacks of traditional spatial clustering, but has a geometric focus. By factoring out the topological aspects of the method we obtain a generic boundary-based clustering that robustly generalizes for arbitrary Minkowski distances and is capable of handling obstacles. We illustrate this with the Manhattan distance and the Dominance distance. Experiments demonstrate that our method consistently finds various types of high-quality clusters within subquadratic time.