Application of An Improved K-means Clustering Algo-rithm in Intrusion Detection
Dongmei Yu, Guoli Zhang, Hui Chen
Available Online September 2016.
- https://doi.org/10.2991/iccia-16.2016.58How to use a DOI?
- K-Means algorithm; Clustering center; Clustering analysis.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/iccia-16.2016.58 ID - Yu2016/09 ER -