-Microarray technology has been the leading research direction in medicine, pharmacology, genome studies and other related areas over the past years. This technology enables researches to simultaneously study activity expression of tens of thousands of genes. After the experimental data have been processed, arrays of numerical values of gene expressions are obtained that are the basis for receiving relevant information and new knowledge. This paper briefly overviews the basics of microarray technology as well as task classes that could be solved using microarray data. The existing approaches to clustering gene expression sets are discussed. It is shown that the fuzzy cmeans clustering method appears the most appropriate for that purpose. Due to that, the problem of choosing an optimal size of fuzziness parameter arises. Three widespread techniques for solving the problem are considered and their comparative analysis is provided.

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Different types of uncertainty are widely spread in all areas of human activity. Probabilistic uncertainties are related to the chances of occurrence of random events. To deal with this kind of uncertainty, statistics and probability theory are successfully employed. Another kind of uncertainty, fuzzy uncertainties refer to imprecision and fuzziness of different kinds of measurements. To cope with this kind of uncertainty, the fuzzy set theory is used. This paper addresses widespread approaches to combining probabilistic and fuzzy uncertainties. The theoretical fundamentals of the approaches are considered within the context of the generalized theory of uncertainty (GTU).