Elements of information theory
Elements of information theory
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Flexible discriminant and mixture models
Statistics and neural networks
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Self-Organizing Maps
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Variational Extensions to EM and Multinomial PCA
ECML '02 Proceedings of the 13th European Conference on Machine Learning
The Journal of Machine Learning Research
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Linear and nonlinear projective nonnegative matrix factorization
IEEE Transactions on Neural Networks
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A distributional clustering model for continuous data is reviewed and new methods for optimizing and regularizing it are introduced and compared. Based on samples of discrete-valued auxiliary data associated to samples of the continuous primary data, the continuous data space is partitioned into Voronoi regions that are maximally homogeneous in terms of the discrete data. Then only variation in the primary data associated to variation in the discrete data affects the clustering; the discrete data ''supervises'' the clustering. Because the whole continuous space is partitioned, new samples can be easily clustered by the continuous part of the data alone. In experiments, the approach is shown to produce more homogeneous clusters than alternative methods. Two regularization methods are demonstrated to further improve the results: an entropy-type penalty for unequal cluster sizes, and the inclusion of a model for the marginal density of the primary data. The latter is also interpretable as special kind of joint distribution modeling with tunable emphasis for discrimination and the marginal density.