关键词: 聚类, 一种经典的基于密度的聚类算法（DBSCAN）, 参数选择, k-均值算法, 未知协议

Abstract: This paper puts forward a field-irrelative method for dynamically selecting the Eps and MinPts parameters for DBSCAN algorithm. The dataset is first crudely clustered with k-means algorithm using maximum and minimum distance initial-centers choosing method. The distance distribution of samples within each k-means cluster is then calculated and analyzed, choosing the distance which allows maximum point-pair numbers as Eps. The MinPts parameter is also calculated according to the confirmed Eps. Also it improves DBSCAN algorithm to dynamically adjust Eps according to the k-means cluster to which the current key point belongs. Appling the above ideas to unknown protocol bitstreams clustering, the experiment results demonstrate that the improved DBSCAN can yield satisfied clustering results without manually specifying the Eps and MinPts parameters. The applicability and accuracy of DBSCAN algorithm are improved.

Key words: clustering, Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm, parameter selection, k-means algorithm, unknown protocol