Constraint-based clustering in large databases
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Clustering Spatial Data when Facing Physical Constraints
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Spatial clustering is an important research topic in Spatial Data Mining (SDM). In this paper, we propose a particle swarm optimization (PSO) method for Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use PSO algorithm via MAKLINK graphic to get the optimal obstructed path, and then we developed PSO K-Medoids SCOC (PKSCOC) algorithm to cluster spatial data with obstacles constraints. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering.