X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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We propose an information-theoretic clustering framework for density-based clustering and similarity or distance-based clustering with objective functions of clustering performance derived from stochastic complexity and minimum description length (MDL) arguments. Under this framework, the number of clusters and parameters can be determined in a principled way without prior knowledge from users. We show that similarity-based clustering can be viewed as combinatorial optimization on graphs. We propose two clustering algorithms, one of which relies on a minimum arborescence tree algorithm which returns optimal clustering under the proposed MDL objective function for similarity-based clustering. We demonstrate clustering performance on synthetic data.