Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering by competitive agglomeration
Pattern Recognition
A novel semi-supervised fuzzy C-means clustering method
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Active constrained clustering with multiple cluster representatives
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Locality sensitive C-means clustering algorithms
Neurocomputing
A semi-supervised fuzzy clustering algorithm applied to gene expression data
Pattern Recognition
A fuzzy minimax clustering model and its applications
Information Sciences: an International Journal
Improving constrained clustering with active query selection
Pattern Recognition
Remote sensing image segmentation by active queries
Pattern Recognition
Semi-supervised clustering with discriminative random fields
Pattern Recognition
Applying a novel decision rule to the semi-supervised clustering method based on one-class SVM
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
LinkFCM: Relation integrated fuzzy c-means
Pattern Recognition
The novel seeding-based semi-supervised fuzzy clustering algorithm inspired by diffusion processes
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Active selection of clustering constraints: a sequential approach
Pattern Recognition
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Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user's expectations. To make such a semi-supervised categorization approach acceptable for the user, this information must be of a very simple nature and the amount of information the user is required to provide must be minimized. We propose here an effective semi-supervised clustering algorithm, active fuzzy constrained clustering (AFCC), that minimizes a competitive agglomeration cost function with fuzzy terms corresponding to pairwise constraints provided by the user. In order to minimize the amount of constraints required, we define an active mechanism for the selection of candidate constraints. The comparisons performed on a simple benchmark and on a ground truth image database show that with AFCC the results of clustering can be significantly improved with few constraints, making this semi-supervised approach an attractive alternative in the categorization of image databases.