Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering

Research on data mining methods of potential risk for large cloud computing network

Authors
Chen Wang
Corresponding Author
Chen Wang
Available Online April 2015.
DOI
https://doi.org/10.2991/amcce-15.2015.274How to use a DOI?
Keywords
cloud computing; potential risk; data mining;
Abstract
During the study process of mining methods for data of potential risk in large cloud computing network, when mining data of risk with traditional methods, principal element features and context features of data have significant fluctuations, which results in inaccurate data mining results. For this, a new large-scale cloud computing networks mining methods for data of potential risk is proposed. The algorithm gives a general definition to abnormal data from the view of geometry, reducing the dimension of vectors characteristics of data of potential risk under cloud computing, feature extraction algorithm is adopted to preprocess data of potential risk, and enlarge the distance between data of potential risk in dense distribution regions of samples, while, shorten the distance between samples in sparse distribution regions of samples, so as to prompt uniform overall distribution of sample library under cloud computing, and achieve accurate mining for data of potential risk in large-scale cloud computing networks. The simulation proved that the proposed mining method for data of potential risk in cloud computing network has higher mining efficiency and accuracy.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Chen Wang
PY  - 2015/04
DA  - 2015/04
TI  - Research on data mining methods of potential risk for large cloud computing network
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.274
DO  - https://doi.org/10.2991/amcce-15.2015.274
ID  - Wang2015/04
ER  -