Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

An Optimization Algorithm of Selecting Initial Clustering Center in K - means

Authors
Tianhan Gao, Xue Kong
Corresponding Author
Tianhan Gao
Available Online June 2016.
DOI
https://doi.org/10.2991/mecs-17.2017.33How to use a DOI?
Keywords
K-means, Initial clustering center, MapReduce, Local density
Abstract
The traditional stand-alone K-means clustering algorithm has the limitation of time consumption and memory overflow when dealing with large-scale data. Although this problem is solved with the help of MapReduce framework. However, the clustering accuracy effect is not stable due to the selection of initial clustering center. Therefore, this paper presents an algorithm for optimizing the initial clustering center in K-means by using several equal-scale sampling, calculating the local density and selecting the optimal initial clustering center. The experimental results show that the optimized algorithm shortens the clustering time and improves the accuracy and stability of clustering procedure in K-means.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-352-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecs-17.2017.33How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Tianhan Gao
AU  - Xue Kong
PY  - 2016/06
DA  - 2016/06
TI  - An Optimization Algorithm of Selecting Initial Clustering Center in K - means
BT  - 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.33
DO  - https://doi.org/10.2991/mecs-17.2017.33
ID  - Gao2016/06
ER  -