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olga.m.pol@yandex.ru, komarov@mgul.ac.ru

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CLUSTER ANALYSIS OF THE GROUP EXPERT INFORMATION

Poleshchuk O.M., Prof. MSFU, Dr. Sci. (Tech.); Komarov E.G., Assoc. Prof. MSFU, Dr. Sci. (Tech.) olga.m.pol@yandex.ru, komarov@mgul.ac.ru Moscow State Forest University (MSFU), 1st Institutskaya st., 1, 141005, Mytischi, Moscow reg., Russia A model of fuzzy clustering analysis has been developed for the study of the structural composition of the expert information. The model allows to cluster the expert information at different levels of confidence. Since the expert information processing often has to deal with linguistic descriptions of the objects, it is necessary not to ignore the arising fuzzy component, but to use methods that allow it to be considered. To analyze the expert information it is insufficient to compare the data obtained from one expert, with the data obtained from another expert. It is necessary to analyze the structural composition of all the expert data and determine the location of each individual expertise in this system. It is insufficient to quantify the similarity of expert data, n interpretation of these indicators on a qualitative level is required. The possibility of a proposed flexible approach to the clustering of expert opinions is essential, because it allows decision-making according to the requirements of reliability. The numerical example has demonstrated that the developed model of fuzzy clustering analysis can be used for analysis of expert group information successfully.

Keywords: clustering analysis, expert group information References

1. Olga Poleshchuk and Evgeniy Komarov Expert Fuzzy Information Processing. Springer-Verlag Berlin Heidelberg, 2011. 237 p.

Zadeh L.A. Ponjatie lingvisticheskoj peremennoj i ego primenenie k prinjatiju priblizitelnyh reshenij [Concept of a linguistic 2.

variable and its application to adoption of approximate decisions]. Moscow: Mir, 1976. 165 p.

3. Litvak B.G. Ekspertnye otsenki i prinyatie resheniy [Expert evaluation and decision-making]. Moscow: Patent, 1996. 271 .

4. Ashraf Darwish and Olga Poleshchuk New models for monitoring and clustering of the state of plant species based on sematic spaces. Journal of Intelligent and Fuzzy Systems. 2014. Vol. 26. pp. 10891094.

5. Tamura S., Higuchi S., Tanaka K. Pattern classification based on fuzzy relations. IEEE Transactions on Systems, Man and Cybernetics. 1971. Vol. SMC-1. pp. 61-66.

6. Zadeh L.A. Similarity relations and fuzzy orderings. Information Sciences. 1971. Vol. 3. pp. 177-200.

7. Ruspini E.H. A new approach to clustering. Information and Control. 1969. Vol. 15. pp. 22-32.

8. Ruspini E.H. Numerical methods for fuzzy clustering. Information Sciences. 1970. Vol. 2. pp. 319-350.

9. Poleshhuk O.M. O razvitii sistem obrabotki nechetkoj informacii na baze polnyh ortogonalnyh semanticheskih prostranstv [On the development of fuzzy information processing systems on the basis of complete orthogonal semantic spaces]. Moscow state forest university bulletin Lesnoy vestnik. 2003. 1 (26). p. 112117.

10. Poleshchuk O. The determination of students fuzzy rating points and qualification levels. International Journal of Industrial and Systems Engineering. 2011. Vol. 9, 1. pp. 3-20.

11. Poleshhuk O.M. Postroenie gruppovoj jekspertnoj ocenki kachestvennyh pokazatelej slozhnyh tehnicheskih sistem [Creation of a group expert assessment of quality indicators of complex technical systems]. Moscow state forest university bulletin Lesnoy vestnik. 2012. 6 (89). pp. 37-40.

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