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Please use this identifier to cite or link to this item: https://oldena.lpnu.ua/handle/ntb/52474
Title: Game Model for Data Stream Clustering
Authors: Kravets, Petro
Affiliation: Lviv Polytechnic National University
Bibliographic description (Ukraine): Kravets P. Game Model for Data Stream Clustering / Petro Kravets // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 123–127. — (Dynamic Data Mining & Data Stream Mining).
Bibliographic description (International): Kravets P. Game Model for Data Stream Clustering / Petro Kravets // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 123–127. — (Dynamic Data Mining & Data Stream Mining).
Is part of: Data stream mining and processing : proceedings of the IEEE second international conference, 2018
Conference/Event: IEEE second international conference "Data stream mining and processing"
Issue Date: 28-Feb-2018
Publisher: Lviv Politechnic Publishing House
Place of the edition/event: Львів
Temporal Coverage: 21-25 August 2018, Lviv
Keywords: data stream clustering
stochastic game model
adaptive game method
Number of pages: 5
Page range: 123-127
Start page: 123
End page: 127
Abstract: In this article, the stochastic game model for data stream clustering is offered. Players represent numerical values of the clustering data. The essence of the game is that players perform a self-learning random move from one cluster to another in order to minimize the differences between the data of the same cluster. To solve the game, an adaptive recursive method has been developed. Computer modeling confirms the convergence of the game method with certain limitations of its parameters.
URI: https://ena.lpnu.ua/handle/ntb/52474
ISBN: © Національний університет „Львівська політехніка“, 2018
© Національний університет „Львівська політехніка“, 2018
Copyright owner: © Національний університет “Львівська політехніка”, 2018
References (Ukraine): [1] A. Jain, M. Murty, and P. Flynn, “Data Clustering: A Review”, ACM Computing Surveys, vol 31, no. 3, pp. 264-323, September 1999.
[2] D. Barbara, “Requirements for clustering data streams”, ACM SIGKDD Explorations Newsletter, vol. 3, №. 2, pp. 23-27, 2003.
[3] J. Chandrika, and K.R. Ananda Kumar, “Dynamic Clustering Of High-Speed Data Streams”, International Journal of Computer Science Issues, vol. 9, iss. 2, №. 1, pp. 224-228, 2012.
[4] T. Roughgarden, E. Tardos and V. V. Vazirani. Algorithmic Game Theory, edited by Noam Nisan, Cambridge University Press, 2007.
[5] A. Nazin, and A. Poznyak, Adaptive Choice of Variants, Moscow, Nauka, 1986 (in Russian).
[6] H. J. Kushner, G. George Yin, Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer Verlag, 2003.
References (International): [1] A. Jain, M. Murty, and P. Flynn, "Data Clustering: A Review", ACM Computing Surveys, vol 31, no. 3, pp. 264-323, September 1999.
[2] D. Barbara, "Requirements for clustering data streams", ACM SIGKDD Explorations Newsletter, vol. 3, №. 2, pp. 23-27, 2003.
[3] J. Chandrika, and K.R. Ananda Kumar, "Dynamic Clustering Of High-Speed Data Streams", International Journal of Computer Science Issues, vol. 9, iss. 2, №. 1, pp. 224-228, 2012.
[4] T. Roughgarden, E. Tardos and V. V. Vazirani. Algorithmic Game Theory, edited by Noam Nisan, Cambridge University Press, 2007.
[5] A. Nazin, and A. Poznyak, Adaptive Choice of Variants, Moscow, Nauka, 1986 (in Russian).
[6] H. J. Kushner, G. George Yin, Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer Verlag, 2003.
Content type: Conference Abstract
Appears in Collections:Data stream mining and processing : proceedings of the IEEE second international conference

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