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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Scalable Clustering Algorithms with Balancing Constraints
Data Mining and Knowledge Discovery
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Revisiting probabilistic models for clustering with pair-wise constraints
Proceedings of the 24th international conference on Machine learning
Top 10 algorithms in data mining
Knowledge and Information Systems
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
K-Means with Large and Noisy Constraint Sets
ECML '07 Proceedings of the 18th European conference on Machine Learning
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
The Journal of Machine Learning Research
Learning low-rank kernel matrices for constrained clustering
Neurocomputing
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
Semi-Supervised Maximum Margin Clustering with Pairwise Constraints
IEEE Transactions on Knowledge and Data Engineering
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The problem of clustering with constraints has received considerable attention in the last decade. Indeed, several algorithms have been proposed, but only a few studies have partially compared their performances. In this work, three well-known algorithms for k-means-based clustering with soft constraints --Constrained Vector Quantization Error CVQE, its variant named LCVQE, and the Metric Pairwise Constrained K-Means MPCK-Means --are systematically compared according to three criteria: Adjusted Rand Index, Normalized Mutual Information, and the number of violated constraints. Experiments were performed on 20 datasets, and for each of them 800 sets of constraints were generated. In order to provide some reassurance about the non-randomness of the obtained results, outcomes of statistical tests of significance are presented. In terms of accuracy, LCVQE has shown to be competitive with CVQE, while violating less constraints. In most of the datasets, both CVQE and LCVQE presented better accuracy compared to MPCK-Means, which is capable of learning distance metrics. In this sense, it was also observed that learning a particular distance metric for each cluster does not necessarily lead to better results than learning a single metric for all clusters. The robustness of the algorithms with respect to noisy constraints was also analyzed. From this perspective, the most interesting conclusion is that CVQE has shown better performance than LCVQE in most of the experiments. The computational complexities of the algorithms are also presented. Finally, a variety of more specific new experimental findings are discussed in the paper --e.g., deduced constraints usually do not help finding better data partitions.