Problems of Fuzzy Clustering of Microarray Data

Abstract

-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.

A Modified Case-Based Reasoning Method Based on the Rough Set Theory

“…The above records are interpreted as follows [10]: -if x i belongs to the R-positive region, then it is identified with complete certainty as belonging to this class;…”

“…Let's first consider RST basic concepts, which are necessary for presentation knowledge about cases, forming the CB and subsequent searching of cases (for complete information on RST, see [8,9,10]).…”

“…Such family is called a knowledge base on U, which represents a set of basic aspects of classification (color, temperature, etc.) [10].…”