Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
Locality sensitive semi-supervised feature selection
Neurocomputing
The coefficient of concordance for vague data
Computational Statistics & Data Analysis
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Constrained laplacian score for semi-supervised feature selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Local-to-global semi-supervised feature selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Constraint Score Evaluation for Spectral Feature Selection
Neural Processing Letters
Active selection of clustering constraints: a sequential approach
Pattern Recognition
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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Recent feature selection scores using pairwise constraints (must-link and cannot-link) have shown better performances than the unsupervised methods and comparable to the supervised ones. However, these scores use only the pairwise constraints and ignore the available information brought by the unlabeled data. Moreover, these constraint scores strongly depend on the given must-link and cannot-link subsets built by the user. In this paper, we address these problems and propose a new semi-supervised constraint score that uses both pairwise constraints and local properties of the unlabeled data. Experiments using Kendall's coefficient and accuracy rates, show that this new score is less sensitive to the given constraints than the previous scores while providing similar performances.