Digital image processing
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning with Constrained and Unlabelled Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active semi-supervised fuzzy clustering
Pattern Recognition
Optimizing Kernel Functions Using Transfer Learning from Unlabeled Data
ICMV '09 Proceedings of the 2009 Second International Conference on Machine Vision
Clustering by competitive agglomeration
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images
IEEE Transactions on Fuzzy Systems
Supervised isomap based on pairwise constraints
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Semi-supervised change detection using modified self-organizing feature map neural network
Applied Soft Computing
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Recently semi-supervised fuzzy clustering with pairwise constraints was developed, in which the disagreement on the magnitude order between penalty cost function and the basic objective function will cause over adjustment of membership values and their deviation from the normal range. In order to solve this problem, an improved semi-supervised fuzzy clustering algorithm with pairwise constraints (SCAPC) was proposed based on a redefined objective function. The new penalty cost function in SCAPC theoretically conforms to the methodology of classical fuzzy clustering, which is expressed as the violation cost incurred by the pairs, and has the same magnitude order as the basic objective function. Experimental results on benchmark datasets and images showed that SCAPC can produce more accurate clustering by moderately enhancing or reducing the ambiguous memberships. Research indicates that constraint term of the proposed algorithm can achieve a good agreement and cooperation with the basic objective function.