A Distributed K - means Clustering Algorithm

DOI

10.2991/icadme-17.2017.59How to use a DOI?

Keywords

K-means clustering algorithm; distributed environment; large data set; complexity.

Abstract

This paper presents a distributed clustering algorithm for large data sets. The algorithm is based on the traditional K-means algorithm to make reasonable improvements, make it more suitable for distributed environment, and analysis algorithm from complexity to compare the algorithm with the traditional centralized K-means algorithm and other distributed algorithms. Experiments show that the algorithm improves the data processing speed while keeping all the necessary features of the centralized K-means algorithm.

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  - Guo-Song Jiang
AU  - Xiao-Ling He
PY  - 2017/07
DA  - 2017/07
TI  - A Distributed K - means Clustering Algorithm
BT  - Proceedings of the 2017 7th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 303
EP  - 308
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-17.2017.59
DO  - 10.2991/icadme-17.2017.59
ID  - Jiang2017/07
ER  -