Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017)

Exploration and Practice of Data Mining Course Teaching Oriented Big Data

Authors
Lei Gang, Xiong Chen
Corresponding Author
Lei Gang
Available Online April 2017.
DOI
10.2991/icmse-17.2017.9How to use a DOI?
Keywords
Data Mining. Course teaching. Big Data. System concept.
Abstract

The course Data Mining, one of the core courses in many majors, has evolved from Data Mining oriented Warehouse, Web Data Mining to the current Big Data Mining. This paper has explored course teaching on discussion in the technology of big data mining and course practice in real data mining. We propose a course teaching plan about Data Mining course oriented big data, analyze features of the course teaching plan with the system concept, and describe the teaching practice to postgraduates majored in Management Science and Engineering. The effect of teaching practice is exciting.

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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Volume Title
Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-327-2
ISSN
2352-5401
DOI
10.2991/icmse-17.2017.9How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Lei Gang
AU  - Xiong Chen
PY  - 2017/04
DA  - 2017/04
TI  - Exploration and Practice of Data Mining Course Teaching Oriented Big Data
BT  - Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017)
PB  - Atlantis Press
SP  - 47
EP  - 50
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-17.2017.9
DO  - 10.2991/icmse-17.2017.9
ID  - Gang2017/04
ER  -