Proceedings of the 2015 International Symposium on Computers & Informatics

K-means Clustering Optimization Algorithm Based on MapReduce

Authors
Zhihua Li, Xudong Song, Wenhui Zhu, Yanxia Chen
Corresponding Author
Zhihua Li
Available Online January 2015.
DOI
10.2991/isci-15.2015.29How to use a DOI?
Keywords
Data Mining; K-means Clustering algorithm;MapReduce; Hadoop
Abstract

Aiming at the defects of traditional K-means clustering algorithm for big data, this paper provides K-means clustering mining optimization algorithm based on big data, shows a MapReduce software architecture which is suitable for large data processing mechanism, provides an improved method for selecting initial clustering centers and puts forward a K-means algorithm optimization based on MapReduce model. The improved algorithm is applied to the coal quality analysis, the result shows that compared with traditional algorithms, the optimization algorithm improves the efficiency of the algorithm obviously, and the accuracy is also enhanced.

Copyright
© 2015, 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 2015 International Symposium on Computers & Informatics
Series
Advances in Computer Science Research
Publication Date
January 2015
ISBN
978-94-62520-56-1
ISSN
2352-538X
DOI
10.2991/isci-15.2015.29How to use a DOI?
Copyright
© 2015, 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  - Zhihua Li
AU  - Xudong Song
AU  - Wenhui Zhu
AU  - Yanxia Chen
PY  - 2015/01
DA  - 2015/01
TI  - K-means Clustering Optimization Algorithm Based on MapReduce
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 198
EP  - 203
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.29
DO  - 10.2991/isci-15.2015.29
ID  - Li2015/01
ER  -