Design of data mining model based on improved manifold learning algorithm in cloud computing environment

DOI

10.2991/fmsmt-17.2017.277How to use a DOI?

Keywords

cloud computing; Data; mining model;

Abstract

Efficient data mining model design for a large database in the cloud computing environment is studied. For large databases efficiently mining problem, an efficient data mining model in the cloud computing environment based on improved manifold learning algorithms is proposed. The use of nonlinear manifold learning algorithms is able to reduce dimensionality of data vector feature in cloud computing environments, through characteristic extraction module to preprocess data, improved classical manifold learning algorithm is adopted to increase the distance between the data of sample spread intensive area and shorten the distance between the data of sample spread sparse area, prompting even overall distribution of sample database under cloud computing environment, so as to achieve accurate mining for efficient data in cloud computing environment. The experimental results show that the proposed method can accurately mine target data under cloud computing environments, with high efficiency and precision.

Copyright

© 2017, 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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TY  - CONF
AU  - Zhan-kun Zhao
PY  - 2017/04
DA  - 2017/04
TI  - Design of data mining model based on improved manifold learning algorithm in cloud computing environment
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 1421
EP  - 1424
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.277
DO  - 10.2991/fmsmt-17.2017.277
ID  - Zhao2017/04
ER  -