Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
A graph-based projection approach for semi-supervised clustering
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Toward improving re-coloring based clustering with graph b-coloring
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A graph model for clustering based on mutual information
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Toward finding hidden communities based on user profile
Journal of Intelligent Information Systems
A re-coloring approach for graph b-coloring based clustering
International Journal of Knowledge-based and Intelligent Engineering Systems
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Semi-supervised learning has been attracting much interest to cope with vast amount of data. When similarities among instances are specified, by connecting each pair of instances with an edge, the entire data can be represented as an edge-weighted graph. Based on the graph representation, we have proposed a graph-based approach for semi-supervised clustering, which modifies the graph structure by contraction in graph theory and graph Laplacian in spectral graph theory. In this paper we conduct extensive experiments over various document datasets and report its performance evaluation, with respect to the type of constraints as well as the number of constraints. We also compare it with other state of the art methods in terms of accuracy and running time, and the results are encouraging. Especially, our approach can leverage small amount of pairwise constraints to increase the performance.