Algorithms for clustering data
Algorithms for clustering data
Computing the geodesic center of a simple polygon
Discrete & Computational Geometry
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Clustering Algorithms
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Region-restricted clustering for geographic data mining
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
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Let P be a simple polygon of n vertices and let S be a set of N points lying in the interior of P. A geodesic disk GD(p, r) with center p and radius r is the set of points in P that have a geodesic distance ≤ r from p (where the geodesic distance is the length of the shortest polygonal path connection that lies in P). In this paper we present an output sensitive algorithm for finding all N geodesic disks centered at the points of S, for a given value of r. Our algorithm runs in O((n + (kn)2/3 + k) logc n) time, for some constant c and output size k. It is the basis of a cluster reporting algorithm where geodesic distances are used.