Proceedings of the 2015 Conference on Informatization in Education, Management and Business

Research and Discussion on the Novel Big Data Clustering Algorithm based on Probability Theory and Nash Game Theory

Authors
Liang Haijun
Corresponding Author
Liang Haijun
Available Online September 2015.
DOI
https://doi.org/10.2991/iemb-15.2015.206How to use a DOI?
Keywords
Data Clustering; Probability Theory; Nash Game Theory; Experimental Analysis.
Abstract

In this paper, we conduct research on the novel big data clustering algorithm based on probability theory and Nash game theory. Clustering algorithm is an effective method of data analysis, clustering algorithm is without any prior information of data clustering analysis of data and this kind of algorithm is also known as unsupervised learning methods. The Nash game theory and probability enhance the performance of the traditional clustering algorithm. The experiment result proves the feasibility of the combination. We set the schedule and prospect in the final part.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 Conference on Informatization in Education, Management and Business
Series
Advances in Social Science, Education and Humanities Research
Publication Date
September 2015
ISBN
978-94-6252-105-6
ISSN
2352-5398
DOI
https://doi.org/10.2991/iemb-15.2015.206How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Liang Haijun
PY  - 2015/09
DA  - 2015/09
TI  - Research and Discussion on the Novel Big Data Clustering Algorithm based on Probability Theory and Nash Game Theory
BT  - Proceedings of the 2015 Conference on Informatization in Education, Management and Business
PB  - Atlantis Press
SP  - 1008
EP  - 1012
SN  - 2352-5398
UR  - https://doi.org/10.2991/iemb-15.2015.206
DO  - https://doi.org/10.2991/iemb-15.2015.206
ID  - Haijun2015/09
ER  -