Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

The Application of Machine Learning in Data Mining under Big Data Environment

Authors
Weini Chen
Corresponding Author
Weini Chen
Available Online May 2018.
DOI
10.2991/ncce-18.2018.196How to use a DOI?
Keywords
big data; machine learning; data mining; application.
Abstract

With the development of economic globalization, the rapid development of industries in various fields, big data technology has attracted more and more attention. Network data is constantly being generated at an unprecedented rate, and it is necessary to intelligently process huge data, and then to make full use of the value in the data, you need to use machine learning methods. This paper describes the related theory of machine learning in detail. Based on data mining, it discusses the flow chart of neural network training algorithm in detail and studies the application and prospect of machine learning in big data.

Copyright
© 2018, 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 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Series
Advances in Intelligent Systems Research
Publication Date
May 2018
ISBN
978-94-6252-517-7
ISSN
1951-6851
DOI
10.2991/ncce-18.2018.196How to use a DOI?
Copyright
© 2018, 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  - Weini Chen
PY  - 2018/05
DA  - 2018/05
TI  - The Application of Machine Learning in Data Mining under Big Data Environment
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 1162
EP  - 1165
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.196
DO  - 10.2991/ncce-18.2018.196
ID  - Chen2018/05
ER  -