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.

Peilun Han, Junxiu An. An Improved Ball K-Means Clustering Method Based on SOM. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 6: 80-83. https://doi.org/10.25236/AJCIS.2023.060612.

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