ESTIMATION OF THE INDUCTIVE MODEL OF OBJECTS CLUSTERING STABILITY BASED ON THE K-MEANS ALGORITHM FOR DIFFERENT LEVELS OF DATA NOISE

Authors

  • S. A. Babichev Jan Evangelista Purkyne University in Usti nad Labem, Czech Republic, Czechia
  • V. I. Lytvynenko Kherson National Technical University, Kherson, Ukraine, Ukraine
  • M. A. Taif Kherson National Technical University, Kherson, Ukraine,

DOI:

https://doi.org/10.15588/1607-3274-2016-4-7

Keywords:

inductive modeling, clustering, k-means algorithm, external balance criterion.

Abstract

The inductive model of the objective clustering of objects based on the k-means algorithm clustering is presented in the paper. The algorithm for division of initial data into two equal power subsets is proposed and practically implemented. The difference between the mass centres of the appropriate clusters in different clustering is proposed to use as an external balance criterion. Approbation of the proposed model operation was carried out using the data “Compound” and “Aggregation” of the database of the Computing School in the Eastern Finland University. The researches on the estimation of the model stability to a noise component using the data “Seeds” are presented in the paper. The algorithms k-means, c-means, inductive k-means and agglomerative hierarchical algorithm were used to compare the results of the experiment. The ways of further improvement of the proposed model in order to increase the objectivity of investigated data clustering were defined by the results of the simulation.

References

Івахненко О. Г. Метод групового урахування аргументів – конкурент методу стохастичної апроксимації / О. Г. Івахненко // Автоматика. – 1968. – № 3. – С. 58–72. 2. Ивахненко А. Г. Индуктивный метод самоорганизации моделей сложных систем / А. Г. Ивахненко. – К. : Наук. думка, 1982. – 296 с. 3. Ивахненко А. Г. Объективная кластеризация на основе теории самоорганизации моделей / А. Г. Ивахненко // Автоматика. – 1987. – №5. – С. 6–15. 4. Madala H. R. Inductive Learning Algorithms for Complex Systems Modeling / H. R. Madala, A. G. Ivakhnenko. – CRC Press, 1994. – 365 p. 5. Степашко В. С. Теоретические аспекты МГУА как метода индуктивного моделирования / В. С. Степашко // Управляющие системы и машины (УСиМ). – 2003. – № 2. – С. 31–38. 6. Степашко В. С. Елементи теорії індуктивного моделювання – Стан та перспективи розвитку інформатики в Україні : монографія / Колектив авторів / В. С. Степашко. – К. : Наукова думка, 2010. – 1008 с. – С. 471–486. 7. Осипенко В. В. Два підходи до розв’язання задачі кластерізації у широкому сенсі з позицій індуктивного моделювання / В. В. Осипенко // Енергетика і Автоматика. – 2014. – № 1. – С. 83–97. 8. Osypenko V. V. The Methodology of Inductive System Analysis as a Tool of Engineering Researches Analytical Planning / V. V. Osypenko, V. M. Reshetjuk // Ann. Warsaw Univ. Life Sci. – SGGW. – 2011. – № 58. – P. 67–71. 9. Сарычева Л. В. Объективный кластерный анализ данных на основе метода группового учета аргументов / Л. В. Сарычева // Проблемы управления и автоматики. – 2008. – № 2. – С. 86–104. 10. The Using of Biclustering Techniques in Inductive Modeling Systems of Biological Processes / [S. Babichev, V. Osypenko, M. A. Taif, V. Lytvynenko] // Inductive modeling of complex systems. – 2015. – № 7. – P. 5–14. 11. https://cs.joensuu.fi/sipu/datasets/ 12. http://archive.ics.uci.edu/ml/datasets/seeds

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How to Cite

Babichev, S. A., Lytvynenko, V. I., & Taif, M. A. (2017). ESTIMATION OF THE INDUCTIVE MODEL OF OBJECTS CLUSTERING STABILITY BASED ON THE K-MEANS ALGORITHM FOR DIFFERENT LEVELS OF DATA NOISE. Radio Electronics, Computer Science, Control, (4). https://doi.org/10.15588/1607-3274-2016-4-7

Issue

No. 4 (2016): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

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Copyright (c) 2017 S. A. Babichev, V. I. Lytvynenko, M. A. Taif

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