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The problem of automatic classification of scientific texts is considered. Methods based on statistical analysis of probabilistic distributions of scientific terms in texts are discussed. The procedures for selecting the most informative terms and the method of making use of auxiliary information related to the terms positions are presented. The results of experimental evaluation of proposed algorithms and procedures over real-world data are reported. [ABSTRACT FROM AUTHOR]

This paper discusses a soft sample clustering problem for multivariate independent random data satisfying the mixture model of the Gaussian distribution. The theory recommends to estimate the parameters of model by the maximum likelihood method and to use “plug‐in” approach for data clustering. Unfortunately, the calculation problem of the maximum likelihood estimate is not completely solved in multivariate case. This work proposes a new constructive a few stage procedure to solve this task. This procedure includes statistical distribution analysis of a large number of the univariate projections of observations, geometric clustering of a multivariate sample and application of EM algorithm. The results of the accuracy analysis of the proposed methods is made by means of Monte‐Carlo simulation. [ABSTRACT FROM AUTHOR]