EVOLUTIONARY CLUSTERING AS TECHNIQUE OF ECONOMICS PROBLEMS SOLVING

Authors

  • V. Y. Snytyuk Taras Shevchenko National University of Kyiv
  • O. O. Suprun Institute of Mathematical Machines and Systems Problems, NASU, Kyiv

DOI:

https://doi.org/10.18372/1990-5548.54.12333

Keywords:

Clustering problem, complex objects, evolution technologies, genetic algorithm, evolution strategies

Abstract

The article presents the method, developed to use evolutionary technologies for clustering large amount of objects that are specified by their characteristics values. The need to analyze big data and to extract the necessary data from multidimensional databases makes the classic methods ineffective, or they require a lot of recourses or time to give an appropriate solution to the stated practical problem. Such problems very often appear in economical and financial spheres, where an expert has to make right decisions, based on various information from different sources, this information may have noise effects, or even be unreliable. Solving these problems requires gathering and formalization of available information that can take a lot of time. The presented method allows to use evolutionary technologies, such as genetic algorithms and evolution strategies elements to solve clustering problems with minimal constraints on the initial data – the situation that represents real practical problems. The experimental results of using the method are given, which proof the effectiveness of the proposed methods.

Author Biographies

V. Y. Snytyuk, Taras Shevchenko National University of Kyiv

Intellectual and Information Systems Department

Doctor of Engineering Science. Professor

O. O. Suprun, Institute of Mathematical Machines and Systems Problems, NASU, Kyiv

Post-graduate student

References

I. Mandel, “Cluster analysis,” Finance and Statistics, Moscow, 1988.

A. N. Gorban and A. Yu. Zinovyev, “Method of Elastic Maps and its Applications in Data Visualization and Data Modeling,” Int. Journal of Computing Anticipatory Systems, CHAOS, vol. 12, pp. 353–369, 2002.

T. Kohonen, “Self-organization and associative memory,” New-York, 2d. ed., Springer Verlag, 1988.

A. Jain, M. Murty, and P. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, 1999.

N. G. Zagoruiko, Applied methods of data and knowledge analysis, Novosibirsk, MI SB RAS, 1999, (in Russian).

M. Aizerman, E. Braverman, and A. Rozonoer, Method of potential functions in learning theory, Moskow, Nauka, 1970, (in Russian).

A. Ryosuke, M. Miyamoto, E. Yasunori, and H. Yukihiro, “Hierarchical clustering algorithms with automatic estimation of the number of clusters,” 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), Otso, Japan, 2017.

A. T. C. Francisco, “Fuzzy clustering algorithm with automatic variable selection and entropy regularization,” IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 2017.

M. Anusha and J. G. R. Sathiaseelan, “Evolutionary clustering algorithm using criterion-knowledge-ranking for multi-objective optimization,” World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, India, 2016.

K. S. S. Reddy and C. S. Bindu, “A review on density-based clustering algorithms for big data analysis,” International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) Palladam, Tamilnadu, India, 2017.

S. Yadav and M. Biswas, “Improved color-based K-mean algorithm for clustering of satellite image,” 4th International Conference on Signal Processing and Integrated Networks (SPIN) Noida, Delhi-NCR, India, 2017.

H. Yu, Z. Chang, and B. Zhou, “A novel three-way clustering algorithm for mixed-type data,” IEEE International Conference on Big Knowledge (ICBK), Hefei, China, 2017.

E. Falkenauer, Genetic algorithms and grouping problems, Chichester, England: John Wiley & Sons Ltd., 1997.

H.-P. Schwefel, Evolution and optimum seeking, New York: Wiley & Sons, 1995.

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MATHEMATICAL MODELING OF PROCESSES AND SYSTEMS