Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Correlation Clustering: maximizing agreements via semidefinite programming
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Machine Learning
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Error bounds for correlation clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Correlation clustering in general weighted graphs
Theoretical Computer Science - Approximation and online algorithms
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Bounding and comparing methods for correlation clustering beyond ILP
ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
Improved consensus clustering via linear programming
ACSC '10 Proceedings of the Thirty-Third Australasian Conferenc on Computer Science - Volume 102
Pairwise probabilistic clustering using evidence accumulation
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Probabilistic Clustering Using the Baum-Eagon Inequality
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Overlapping Correlation Clustering
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be captured. We also show that a known optimization heuristic can be applied to the problem formulation, but with the automatic selection of the number of classes. Additionally, we propose a simple way of building an ensemble of agreement functions sampled from a reproducing kernel Hilbert space, which allows to apply correlation clustering without the empirical estimation of pairwise correlation values.