Unsupervised Optimal Fuzzy Clustering
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
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
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Active semi-supervised fuzzy clustering
Pattern Recognition
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
Clustering with local and global regularization
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Graph-optimized locality preserving projections
Pattern Recognition
Semi-supervised distance metric learning for collaborative image retrieval and clustering
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Bioinformatics
Semi-supervised sparse metric learning using alternating linearization optimization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Data clustering with size constraints
Knowledge-Based Systems
An adaptive kernel method for semi-supervised clustering
ECML'06 Proceedings of the 17th European conference on Machine Learning
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
Unsupervised non-parametric kernel learning algorithm
Knowledge-Based Systems
Spatial interaction - modification model and applications to geo-demographic analysis
Knowledge-Based Systems
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Existing methods for semi-supervised fuzzy c-means (FCMs) suffer from the following issues: (1) the Euclidean distance tends to work poorly if each feature of the instance is unequal variance as well as correlation from others and (2) it is generally uneasy to assign an appropriate value for the parameter m involved in their objective function. To address these problems, we develop a novel semi-supervised metric-based fuzzy clustering algorithm called SMUC by introducing metric learning and entropy regularization simultaneously into the conventional fuzzy clustering algorithm. More specifically, SMUC focuses on learning a Mahalanobis distance metric from side information given by the user to displace the Euclidean distance in FCM-based methods. Thus, it has the same flavor as typical supervised metric algorithms, which makes the distance between instances within a cluster smaller than that between instances belonging to different clusters. Moreover, SMUC introduces maximum entropy as a regularized term in its objective function such that its resulting formulas have the clear physical meaning compared with the other semi-supervised FCM methods. In addition, it naturally avoids the choice on the parameter m due to such a maximum-entropy regularizer. The experiments on real-world data sets show the feasibility and effectiveness of the proposed method with encouraging results.