A Learning Algorithm for the Optimum-Path Forest Classifier
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Semi-supervised metric learning using pairwise constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
Background knowledge integration in clustering using purity indexes
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Clustering with relative constraints
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised clustering with discriminative random fields
Pattern Recognition
Semi-supervised change detection using modified self-organizing feature map neural network
Applied Soft Computing
Subspace clustering of high-dimensional data: an evolutionary approach
Applied Computational Intelligence and Soft Computing
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Semi-supervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depend on the type of supervision and also on the kind of dissimilarity measure used while creating partitions of the space. This paper proposes a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to z. The proposed clustering algorithm simultaneously learns the underlying dissimilarity measure while finding compact clusters in the given data set using relative comparisons. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy and is more robust than similar algorithms using pairwise constraints for supervision.