Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)

Discussion and Improvement of Apriori Algorithm of Data Mining Based on Hadoop Platform

Authors
Mengyang Zhao, Bo Tang, Le Yang
Corresponding Author
Mengyang Zhao
Available Online April 2017.
DOI
10.2991/fmsmt-17.2017.38How to use a DOI?
Keywords
Apriori algorithm, data mining, Hadoop platform
Abstract

Apriori algorithm can accurately find out the related items in the database, and it can be used in various fields of work and life. However, with the explosive growth of data, there are still some disadvantages in the practical application of the algorithm. This paper introduces the Hadoop platform, explores the characteristics of Apriori algorithm, and puts forward a kind of improved Apriori algorithm based on the Hadoop platform to provide some references for the relative researchers.

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 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
Series
Advances in Engineering Research
Publication Date
April 2017
ISBN
978-94-6252-331-9
ISSN
2352-5401
DOI
10.2991/fmsmt-17.2017.38How 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  - Mengyang Zhao
AU  - Bo Tang
AU  - Le Yang
PY  - 2017/04
DA  - 2017/04
TI  - Discussion and Improvement of Apriori Algorithm of Data Mining Based on Hadoop Platform
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 183
EP  - 187
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.38
DO  - 10.2991/fmsmt-17.2017.38
ID  - Zhao2017/04
ER  -