| DC Field | Value | Language |
|---|
| dc.contributor.author | Kravets, Petro | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:04:56Z | - |
| dc.date.available | 2020-06-19T12:04:56Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | 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). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52474 | - |
| dc.description.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. | |
| dc.format.extent | 123-127 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.subject | data stream clustering | |
| dc.subject | stochastic game model | |
| dc.subject | adaptive game method | |
| dc.title | Game Model for Data Stream Clustering | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.format.pages | 5 | |
| dc.identifier.citationen | 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). | |
| dc.relation.references | [1] A. Jain, M. Murty, and P. Flynn, “Data Clustering: A Review”, ACM Computing Surveys, vol 31, no. 3, pp. 264-323, September 1999. | |
| dc.relation.references | [2] D. Barbara, “Requirements for clustering data streams”, ACM SIGKDD Explorations Newsletter, vol. 3, №. 2, pp. 23-27, 2003. | |
| dc.relation.references | [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. | |
| dc.relation.references | [4] T. Roughgarden, E. Tardos and V. V. Vazirani. Algorithmic Game Theory, edited by Noam Nisan, Cambridge University Press, 2007. | |
| dc.relation.references | [5] A. Nazin, and A. Poznyak, Adaptive Choice of Variants, Moscow, Nauka, 1986 (in Russian). | |
| dc.relation.references | [6] H. J. Kushner, G. George Yin, Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer Verlag, 2003. | |
| dc.relation.referencesen | [1] A. Jain, M. Murty, and P. Flynn, "Data Clustering: A Review", ACM Computing Surveys, vol 31, no. 3, pp. 264-323, September 1999. | |
| dc.relation.referencesen | [2] D. Barbara, "Requirements for clustering data streams", ACM SIGKDD Explorations Newsletter, vol. 3, №. 2, pp. 23-27, 2003. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [4] T. Roughgarden, E. Tardos and V. V. Vazirani. Algorithmic Game Theory, edited by Noam Nisan, Cambridge University Press, 2007. | |
| dc.relation.referencesen | [5] A. Nazin, and A. Poznyak, Adaptive Choice of Variants, Moscow, Nauka, 1986 (in Russian). | |
| dc.relation.referencesen | [6] H. J. Kushner, G. George Yin, Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer Verlag, 2003. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 123 | |
| dc.citation.epage | 127 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
