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Clustering based on conditional distributions in an auxiliary space
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Constrained K-means Clustering with Background Knowledge
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Semi-supervised Clustering by Seeding
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Clustering with Qualitative Information
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Multiclass Spectral Clustering
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Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel k-means: spectral clustering and normalized cuts
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
Locally linear metric adaptation for semi-supervised clustering
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Learning with Constrained and Unlabelled Data
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The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
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Semi-supervised graph clustering: a kernel approach
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Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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A discriminative learning framework with pairwise constraints for video object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
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Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised sparse metric learning using alternating linearization optimization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A spectral approach to clustering numerical vectors as nodes in a network
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Learning shape segmentation using constrained spectral clustering and probabilistic label transfer
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
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Expert Systems with Applications: An International Journal
Learning low-rank kernel matrices for constrained clustering
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Semi-supervised parameter-free divisive hierarchical clustering of categorical data
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Multi-task clustering via domain adaptation
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Semi-supervised agglomerative hierarchical clustering with ward method using clusterwise tolerance
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IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Efficient semi-supervised learning on locally informative multiple graphs
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Semi-Supervised kernel clustering with sample-to-cluster weights
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Fast semi-supervised clustering with enhanced spectral embedding
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International Journal of Business Intelligence and Data Mining
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MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Semi-supervised clustering via multi-level random walk
Pattern Recognition
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Pattern Recognition Letters
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Pattern Recognition Letters
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Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semi-supervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semi-supervised algorithms on both vector-based and graph-based data sets.