Sharp, reliable predictions using supervised mixture models
Sharp, reliable predictions using supervised mixture models
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Lagrangian support vector machines
The Journal of Machine Learning Research
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This paper describes an algorithm that is an extension of mixture-modelling to supervised clustering. It is demonstrated to be as accurate as current state-of-the-art machine learning algorithms across various data sets, and significantly more accurate than distance-based supervised clustering algorithms. Most significantly, it combines the classification itself with the calculation of rich information about the probabilities of class membership, the significance of attributes in relation to a classification, and the data space described by the data items and attributes.