An Improved Ball K-Means Clustering Method Based on SOM

Abstract

With the increase of the dimension and quantity of sample data, the calculation cost of K-Means clustering algorithm increases sharply. Therefore, a novel accelerated accurate K-Means clustering algorithm, called "Ball K-Means", has recently been used to reduce the computational cost. Although Ball K-Means reduces the computational cost, both this algorithm and K-Means algorithm lack the global search capability. K-means algorithm may fall into local minima because of its dependence on the initial center. The proper selection of the initial center vector becomes the key to improve the K-means algorithm. Therefore, self-organizing map (SOM) can be used to cluster and determine the clustering range quickly, and then the result can be used as the initial center vector of K-means method. Aiming at the problems that the initial clustering center of Ball K-Means algorithm is randomly selected in the stage of clustering calculation, and the clustering result may fall into a local optimal solution, this article uses SOM network to preliminarily process the data to obtain the initial clustering center of Ball K-Means algorithm, which significantly improves the clustering effect of the algorithm. Taking intrusion detection as an example, the effectiveness and superiority of the algorithm are verified by experiments.