Mahalanobis Distance-Based K-Means Clustering Algorithm for Intrusion Detection
Zhang Wei · Journal of Jiangxi Normal University · 2012
The classic K-means clustering algorithm is based on the Euclidean distance,it applies only to spherical structure clustering and in the processing of data without regard to the correlation between variables and differences in the importance of each variable.To solve the above problem,this paper propose a feasible clustering algorithm,it combines Mahalanobis distance with the K-means and adds a variable weighting factor and a regulating factor of covariance matrix to each class in the objective function.Using the advantage of Mahalanobis distance,it effectively solves the shortcomings of K-means clustering algorithm.Experimental results of date clustering illustrate its feasibility and effectiveness.