Stochastic Voting Algorithms for Web Services Group Testing
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
OCRS: an Interactive Object-based Image Clustering and Retrieval System
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
OCRS: an interactive object-based image clustering and retrieval system
Multimedia Tools and Applications
How evolutionary algorithms are applied to statistical natural language processing
Artificial Intelligence Review
Evolutionary clustering of relational data
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Evolutionary fuzzy clustering of relational data
Theoretical Computer Science
Region-based image clustering and retrieval using multiple instance learning
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Towards hierarchical clustering
CSR'07 Proceedings of the Second international conference on Computer Science: theory and applications
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Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. The identification of clusters in spatially referenced data provides a means of generalization of the spatial component of the data associated with a Geographical Information System. A variety of clustering formulations exists. A non-hierarchical approach in Data-mining applications is to use a medoid based version. This approach has robust behavior with respect to outliers and many heuristics have been developed that find near optimal partitions. This paper develops a genetic search heuristic for solving medoid based clustering problems. We base our genetic recombination upon Random Assorting Recombination. A comparison is made with previous solution approaches. Results show improvements on the genetic search heuristic.