Improved K-Means Algorithm Using in Anomaly Detection
Zhuan Chen · 2015
In order to increase the performance of the K-means algorithm in anomaly detection,an improved K-means algorithm was proposed. The algorithm selected the initial cluster centers according to maximum distance,and the information entropy was introduced to calculate the weight of every attribute,and then the improved weighted Euclidean distance formula was used to calculate the distance between sample points in dataset. The performance of the improved algorithm was tested by KDD CUP99 dataset. The experimental results show that this algorithm will be helpful to increase the detection rate and decrease the false alarm rate in anomaly detection.