Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Self-Organizing Maps
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
The complexity of non-hierarchical clustering with instance and cluster level constraints
Data Mining and Knowledge Discovery
Convergence and Ordering of Kohonen's Batch Map
Neural Computation
Efficient incremental constrained clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Semi-Supervised Learning
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
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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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In this paper, we propose to adapt the batch version of selforganizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones.