Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems
The Journal of Machine Learning Research
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Semi-Supervised Learning
A graph-based projection approach for semi-supervised clustering
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Pairwise constraint propagation for graph-based semi-supervised clustering
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
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Side information such as pairwise constraints is useful to improve the clustering performance in general. However, constraints are not always error free in general. When erroneous constraints are specified as side information, treating them as hard constraints could have the disadvantage since strengthening incorrect or erroneous constraints can lead to performance degradation. In this paper we conduct extensive experiments to investigate the influence of erroneous pairwise constraints over various document datasets. Several state-of-the-art semi-supervised clustering methods with graph representation were evaluated with respect to the type of constraints as well as the number of constraints. Experimental results confirmed that treating pairwise constraints as hard constraints is vulnerable to erroneous ones. However, the results also revealed that the influence of erroneous constraints depends on how the constraints are exploited inside a learning algorithm.