Application of An Improved K-means Clustering Algo-rithm in Intrusion Detection
Dongmei Yu, Guoli Zhang, Hui Chen · 2016
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 calculates 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 algorithm has been proposed.The experimental results show that the improved algorithm greatly improves the stability and the computation efficiency of the algorithm.