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
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Background knowledge integration in clustering using purity indexes
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
Fuzzy semi-supervised co-clustering for text documents
Fuzzy Sets and Systems
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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.