Algorithms for clustering data
Algorithms for clustering data
Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The complexity of non-hierarchical clustering with instance and cluster level constraints
Data Mining and Knowledge Discovery
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Constrained graph b-coloring based clustering approach
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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This paper describes a new topological map dedicated to clustering under probabilistic constraints. In general, traditional clustering is used in an unsupervised manner. However, in some cases, background information about the problem domain is available or imposed in the form of constraints in addition to data instances. In this context, we modify the popular GTM algorithm to take these "soft" constraints into account during the construction of the topology. We present experiments on synthetic known databases with artificial generated constraints for comparison with both GTM and another constrained clustering methods.