Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth 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
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
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Revisiting probabilistic models for clustering with pair-wise constraints
Proceedings of the 24th international conference on Machine learning
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An adaptive kernel method for semi-supervised clustering
ECML'06 Proceedings of the 17th European conference on Machine Learning
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Convergence of GCM and its application to face recognition
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Face recognition using consistency method and its variants
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Learning low-rank kernel matrices for constrained clustering
Neurocomputing
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Improving constrained clustering with active query selection
Pattern Recognition
Semi-supervised clustering with discriminative random fields
Pattern Recognition
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
Regularized soft K-means for discriminant analysis
Neurocomputing
Fuzzy semi-supervised co-clustering for text documents
Fuzzy Sets and Systems
Frontiers of Computer Science: Selected Publications from Chinese Universities
Semi-supervised clustering via multi-level random walk
Pattern Recognition
Active selection of clustering constraints: a sequential approach
Pattern Recognition
A size-insensitive integrity-based fuzzy c-means method for data clustering
Pattern Recognition
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Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiveness of clustering. In this paper, we propose an adaptive Semi-supervised Clustering Kernel Method based on Metric learning (SCKMM) to mitigate the above problems. Specifically, we first construct an objective function from pairwise constraints to automatically estimate the parameter of the Gaussian kernel. Then, we use pairwise constraint-based K-means approach to solve the violation issue of constraints and to cluster the data. Furthermore, we introduce metric learning into nonlinear semi-supervised clustering to improve separability of the data for clustering. Finally, we perform clustering and metric learning simultaneously. Experimental results on a number of real-world data sets validate the effectiveness of the proposed method.