Algorithms for clustering data
Algorithms for clustering data
A survey of constrained classification
Computational Statistics & Data Analysis
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A local search approximation algorithm for k-means clustering
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometry—SoCG2002
Learning with Constrained and Unlabelled Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
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In this paper, we adapt Tuy's concave cutting plane method to the problem of finding an optimal grouping of semi-supervised clustering. We also give properties of local optimal solutions to the semi-supervised clustering. On test data sets with up to 1500 points, our algorithm typically find a solution with objective value around 2% smaller of the initial function value than that obtained by k-means algorithm within 4 seconds, although the run time is hundred times of that of the k-means algorithm.