Application of An Improved K-means Clustering Algo-rithm in Intrusion Detection

DOI

10.2991/iccia-16.2016.58How to use a DOI?

Keywords

K-Means algorithm; Clustering center; Clustering analysis.

Abstract

For the initial clustering center usually choose the randomness of the problem, the pa-per proposes a new initial clustering center selection method. first, the algorithm calcu-lates the Euclidean distance of all data to the origin of the coordinate, and then evenly divide the k class, at last, the average value of each class is calculated, and the k center is selected by this method. And through the experimental comparison of the improved algorithm with the merits of the original algorithm and the improved k-means algo-rithm has been proposed. The experimental results show that the improved algorithm greatly improves the stability and the computation efficiency of the algorithm.

Copyright

© 2016, 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  - Dongmei Yu
AU  - Guoli Zhang
AU  - Hui Chen
PY  - 2016/09
DA  - 2016/09
TI  - Application of An Improved K-means Clustering Algo-rithm in Intrusion Detection
BT  - Proceedings of the 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016)
PB  - Atlantis Press
SP  - 312
EP  - 318
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-16.2016.58
DO  - 10.2991/iccia-16.2016.58
ID  - Yu2016/09
ER  -