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Journal of Computational and Applied Mathematics
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CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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Data Mining and Knowledge Discovery
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A unified framework for model-based clustering
The Journal of Machine Learning Research
Density-based clustering of uncertain data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Automated cell differentiation in multispectral microscopy
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
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Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
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Data & Knowledge Engineering
IEICE - Transactions on Information and Systems
Discovery of feature-based hot spots using supervised clustering
Computers & Geosciences
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MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
REG^2: a regional regression framework for geo-referenced datasets
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Towards region discovery in spatial datasets
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Locally-scaled spectral clustering using empty region graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Representative-based clustering algorithms are quite popular due to their relative high speed and because of their sound theoretical foundation. On the other hand, the clusters they can obtain are limited to convex shapes and clustering results are also highly sensitive to initializations. In this paper, a novel agglomerative clustering algorithm called MOSAIC is proposed which greedily merges neighboring clusters maximizing a given fitness function. MOSAIC uses Gabriel graphs to determine which clusters are neighboring and approximates non-convex shapes as the unions of small clusters that have been computed using a representative-based clustering algorithm. The experimental results show that this technique leads to clusters of higher quality compared to running a representative clustering algorithm standalone. Given a suitable fitness function, MOSAIC is able to detect arbitrary shape clusters. In addition, MOSAIC is capable of dealing with high dimensional data.