Proceedings of the 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)

Application Analysis of Data Mining in Power Enterprise

Authors
Jian Tang
Corresponding Author
Jian Tang
Available Online January 2016.
DOI
https://doi.org/10.2991/ifeea-15.2016.42How to use a DOI?
Keywords
power enterprise, data mining, big data
Abstract
With the progress of the society and the development of information and communication technology, information system is rapidly expanding in various industries and fields. There are more and more data that these systems collect, process, and accumulate, and the growth rate of data volume is larger and larger, and even using "mass, explosive growth" and other words cannot describe the growth rate of data. Data mining is the technology that excavates information with potential value from vast amounts of data. This information is likely to have potential value, to support decision making, so as to bring benefit for the enterprise, or seek the breach for scientific research. Using the data mining technology with powerful functions can make manufacturing power companies convert data into useful information to help decision-making, so as to gain dominant position in the market competition. The paper briefly introduces the concept and methods of data mining, and analyzes the application and the prospect of data mining in the power enterprise.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)
Part of series
Advances in Engineering Research
Publication Date
January 2016
ISBN
978-94-6252-153-7
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifeea-15.2016.42How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jian Tang
PY  - 2016/01
DA  - 2016/01
TI  - Application Analysis of Data Mining in Power Enterprise
BT  - 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)
PB  - Atlantis Press
SP  - 206
EP  - 209
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeea-15.2016.42
DO  - https://doi.org/10.2991/ifeea-15.2016.42
ID  - Tang2016/01
ER  -