Normalized Cuts and Image Segmentation
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
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
The Journal of Machine Learning Research
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks
Proceedings of the 24th international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of kernel and spectral methods for clustering
Pattern Recognition
Genetic-guided semi-supervised clustering algorithm with instance-level constraints
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Semi-supervised graph clustering: a kernel approach
Machine Learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A semi-supervised fuzzy clustering algorithm applied to gene expression data
Pattern Recognition
Learning Bregman Distance Functions for Semi-Supervised Clustering
IEEE Transactions on Knowledge and Data Engineering
Semi-Supervised Maximum Margin Clustering with Pairwise Constraints
IEEE Transactions on Knowledge and Data Engineering
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
Texture aware image segmentation using graph cuts and active contours
Pattern Recognition
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A key issue of semi-supervised clustering is how to utilize the limited but informative pairwise constraints. In this paper, we propose a new graph-based constrained clustering algorithm, named SCRAWL. It is composed of two random walks with different granularities. In the lower-level random walk, SCRAWL partitions the vertices (i.e., data points) into constrained and unconstrained ones, according to whether they are in the pairwise constraints. For every constrained vertex, its influence range, or the degrees of influence it exerts on the unconstrained vertices, is encapsulated in an intermediate structure called component. The edge set between each pair of components determines the affecting scope of the pairwise constraints. In the higher-level random walk, SCRAWL enforces the pairwise constraints on the components, so that the constraint influence can be propagated to the unconstrained edges. At last, we combine the cluster membership of all the components to obtain the cluster assignment for each vertex. The promising experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our method.