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
Conceptual Spaces for Computer Vision Representations
Artificial Intelligence Review
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
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Fast qualitative reasoning about categories in conceptual spaces
Design and application of hybrid intelligent systems
On k-Nearest Neighbor Voronoi Diagrams in the Plane
IEEE Transactions on Computers
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Reasoning about categories in conceptual spaces
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Multi-level clustering and reasoning about its clusters using region connection calculus
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Representing concepts and their boundaries is a core task in concept management. We introduce a flexible framework for representing concept boundaries and tessellations through the complete higher order Voronoi diagrams within conceptual spaces. The framework provides different levels of concept granularity modelling vagueness and indeterminacy of concept boundaries, effectively supports complex concepts of multi-pieced regions with holes, and robustly supports concept learning and formation. We also outline a generic data structure that systematically supports our concept management framework. Experimental results demonstrate the robustness and flexibility of the proposed framework.