An improved algorithm for constructing kth-order voronoi diagrams
IEEE Transactions on Computers
A simple on-line randomized incremental algorithm for computing higher order Voronoi diagrams
SCG '91 Proceedings of the seventh annual symposium on Computational geometry
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Computational geometry in C
A spatial data mining method by clustering analysis
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
Conceptual Spaces: The Geometry of Thought
Conceptual Spaces: The Geometry of Thought
Machine Learning
On O(N^4) Algorithm to Contstruct all Vornoi Diagrams for K Nearest Neighbor Searching
Proceedings of the 10th Colloquium on Automata, Languages and Programming
Finding Boundary Shape Matching Relationships in Spatial Data
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
On k-Nearest Neighbor Voronoi Diagrams in the Plane
IEEE Transactions on Computers
What is the region occupied by a set of points?
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
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In this paper, we propose a postclustering process that robustly computes cluster regions at different levels of granularity through the complete Order-k Voronoi diagrams. The robustness and flexibility of the proposed method overcome the application-dependency and rigidity of traditional approaches. The proposed cluster tessellation method robustly models monotonic and nonmonotonic cluster growth, and provides fuzzy membership in order to represent indeterminacy of cluster regions. It enables the user to explore cluster structures hidden in a dataset in various scenarios and supports "what-if" and "what-happen" analysis. Tessellated clusters can be effectively used for cluster reasoning and concept learning.