Spatial clustering with obstacles constraints using particle swarm optimization
Proceedings of the 2nd international conference on Scalable information systems
An IACO and HPSO Method for Spatial Clustering with Obstacles Constraints
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Spatial Clustering with Obstacles Constraints by Hybrid Particle Swarm Optimization with GA Mutation
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Effective spatial clustering methods for optimal facility establishment
Intelligent Data Analysis
A Particle Swarm Optimization Method for Spatial Clustering with Obstacles Constraints
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Spatial clustering with obstacles constraints using ant colony and particle swarm optimization
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Spatial clustering based on moving distance in the presence of obstacles
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
A quantum particle swarm optimization used for spatial clustering with obstacles constraints
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Density-based semi-supervised clustering
Data Mining and Knowledge Discovery
Obstacle clustering and outlier detection
Proceedings of the 48th Annual Southeast Regional Conference
GIS enabled service site selection: Environmental analysis and beyond
Information Systems Frontiers
A density-based spatial clustering for physical constraints
Journal of Intelligent Information Systems
Towards an ontology-based spatial clustering framework
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A novel spatial clustering with obstacles constraints based on PNPSO and k-medoids
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
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Clustering spatial data is a well-known problem that hasbeen extensively studied to find hidden patterns or meaningfulsub-groups and has many applications such as satelliteimagery, geographic information systems, medical imageanalysis, etc. Although many methods have been proposedin the literature, very few have considered constraintssuch that physical obstacles and bridges linking clustersmay have significant consequences on the effectiveness ofthe clustering. Taking into account these constraints duringthe clustering process is costly, and the effective modeling ofthe constraints is of paramount importance for good performance.In this paper, we define the clustering problem in thepresence of constraints - obstacles and crossings - and investigateits efficiency and effectiveness for large databases.In addition, we introduce a new approach to model theseconstraints to prune the search space and reduce the numberof polygons to test during clustering. The algorithmDBCluC we present detects clusters of arbitrary shape andis insensitive to noise and the input order. Its average runningcomplexity is O(NlogN) where N is the number of dataobjects.