Fast spatial clustering with different metrics and in the presence of obstacles
Proceedings of the 9th ACM international symposium on Advances in geographic information systems
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Applying genetic algorithms to zone design
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A parallel edge-betweenness clustering tool for Protein-Protein Interaction networks
International Journal of Data Mining and Bioinformatics
Differential Betweenness in Complex Networks Clustering
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Spatial clustering of structured objects
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
| Hi-index | 0.00 |
This paper presents a novel approach for the zone design problem that is based on techniques from the field of complex networks research: community detection by betweenness centrality and label propagation. A new algorithm called Spatial Graph based Clustering by Label Propagation (SGCLAP) is introduced. It can deal with very large spatial clustering problems with time complexity O (n logn ). Besides, we use a parallel version of a betweenness-based community detection algorithm that outputs the graph partitioning that maximizes the so-called modularity metric. Both these methods are put at the centre of an effort to build an open source interactive high performance computing platform to assist researchers working with population data.