Proceedings of the 1998 conference on Advances in neural information processing systems II
Flexible discriminant and mixture models
Statistics and neural networks
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Point-distribution algorithm for mining vector-item patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Computer Science - Research and Development
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We introduce a simple on-line algorithm for clustering paired samples of continuous and discrete data. The clusters are defined in the continuous data space and become local there, while within-cluster differences between the associated, implicitly estimated conditional distributions of the discrete variable are minimized. The discrete variable can be seen as an indicator of relevance or importance guiding the clustering. Minimization of the Kullback-Leibler divergence-based distortion criterion is equivalent to maximization of the mutual information between the generated clusters and the discrete variable. We apply the method to a time series data set, i.e. yeast gene expressions measured with DNA chips, with biological knowledge about the functions of the genes encoded into the discrete variable.