SIAM Review
ACM Computing Surveys (CSUR)
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary semi-supervised fuzzy clustering
Pattern Recognition Letters
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel k-means: spectral clustering and normalized cuts
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
Data Clustering with Partial Supervision
Data Mining and Knowledge Discovery
International Journal of Computer Vision
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust path-based spectral clustering
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
Improving fuzzy clustering of biological data by metric learning with side information
International Journal of Approximate Reasoning
Active semi-supervised fuzzy clustering
Pattern Recognition
Semisupervised Clustering with Metric Learning using Relative Comparisons
IEEE Transactions on Knowledge and Data Engineering
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
Semi-supervised graph clustering: a kernel approach
Machine Learning
Maximum Margin Clustering with Pairwise Constraints
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A global optimization method for semi-supervised clustering
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Partially supervised clustering for image segmentation
Pattern Recognition
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Semi-supervised distance metric learning for collaborative image retrieval and clustering
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Semi-Supervised Learning
Density-based semi-supervised clustering
Data Mining and Knowledge Discovery
The Consistent Labeling Problem: Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Maximum Margin Clustering with Pairwise Constraints
IEEE Transactions on Knowledge and Data Engineering
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Semi-supervised clustering exploits a small quantity of supervised information to improve the accuracy of data clustering. In this paper, a framework for semi-supervised clustering is proposed. This framework is capable of integrating with a traditional clustering algorithm seamlessly, and particularly useful for the application where a traditional clustering is designated to use. In the proposed framework, discriminative random fields (DRFs) are employed to model the consistency between the result of a traditional clustering algorithm and the supervised information with the assumption of semi-supervised learning. The semi-supervised clustering problem is thus formulated as finding the label configuration with the maximum a posteriori (MAP) probability of the DRF. A procedure based on the iterated conditional modes algorithm and a metric-learning algorithm is developed to find a suboptimal MAP solution of the DRF. The proposed approach has been tested against various data sets. Experimental results demonstrate that our approach can enhance the clustering accuracy, and thus prove the feasibility of the proposed approach.