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Journal of the ACM (JACM)
Algorithms for graph partitioning on the planted partition model
Random Structures & Algorithms
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Spectral Partitioning of Random Graphs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Empirical Risk Approximation: An Induction Principle for Unsupervised Learning
Empirical Risk Approximation: An Induction Principle for Unsupervised Learning
Correlation Clustering: maximizing agreements via semidefinite programming
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Machine Learning
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The minimax distortion redundancy in empirical quantizer design
IEEE Transactions on Information Theory
Collaborative partitioning with maximum user satisfaction
Proceedings of the 17th ACM conference on Information and knowledge management
Solution stability in linear programming relaxations: graph partitioning and unsupervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An Evidence Accumulation Approach to Constrained Clustering Combination
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Bounding and comparing methods for correlation clustering beyond ILP
ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
Correlation clustering with noisy input
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Correlation clustering with stochastic labellings
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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This paper presents a learning theoretical analysis of correlation clustering (Bansal et al., 2002). In particular, we give bounds on the error with which correlation clustering recovers the correct partition in a planted partition model (Condon & Karp, 2001; McSherry, 2001). Using these bounds, we analyze how the accuracy of correlation clustering scales with the number of clusters and the sparsity of the graph. We also propose a statistical test that analyzes the significance of the clustering found by correlation clustering.