Decision Trees for Probabilistic Data
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Flow classification by histograms: or how to go on safari in the internet
Proceedings of the joint international conference on Measurement and modeling of computer systems
Mixture decomposition of distributions by copulas in the symbolic data analysis framework
Discrete Applied Mathematics - Ordinal and symbolic data analysis (OSDA 2000)
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Various objects can be summarily described by probability distributions: groups of raw data, paths of stochastic processes, neighborhoods of an image pixel and so on. Dealing with nonparametric distributions, we propose a method for classifying such objects by estimating a finite mixture of Dirichlet distributions when the observed distributions are assumed to be outcomes of a finite mixture of Dirichlet processes. We prove the consistency of such a classification by using the mutual singularity of two distinct Dirichlet processes and the martingale convergence theorem. Moreover, this consistency allows us to use some standard data analysis and statistical methods for analyzing the class labels of these objects. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.