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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >An unknown Protocol improved k-means clustering algorithm based on Pearson distance
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An unknown Protocol improved k-means clustering algorithm based on Pearson distance

机译：一种未知协议，改进了基于Pearson距离的K-Means聚类算法

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摘要

In order to solve the clustering problem of unknown binary protocols, an improved k-means unknown binary protocol clustering method is proposed, which determines the initial clustering center and improves the clustering distance. Firstly, the k value is determined and the clustering center is extracted by using DCBP (Determine the initial clustering center of binary Protocol) algorithm and the change rate of error square, and then the data are clustered by improving the k-means algorithm of distance function. The unknown binary protocol bit stream is divided into different subsets of binary protocols. By improving the k-means algorithm, the Pearson distance improves the accuracy of binary protocol clustering from 96% to 98.9%. The DCBP algorithm helps us to determine the k value accurately. The k value determined in this paper is 5, and the clustering accuracy is 98.9%. The clustering accuracy is 80% when k is 4 and 92.2% when k is 6. And the operation speed of the improved k-means algorithm is better than that of the AGNES algorithm. The algorithm is better adapted to the clustering of unknown binary protocols, and improves the accuracy of clustering and the speed of operation.
机译：为了解决未知二进制协议的聚类问题，提出了一种改进的K-is未知的二进制协议群集方法，其确定初始聚类中心并改善聚类距离。首先，确定k值并通过使用DCBP（确定二进制协议的初始聚类中心）算法和错误方形的变化率来提取聚类中心，然后通过改进距离的K均值算法来聚类数据功能。未知的二进制协议比特流被划分为二进制协议的不同子集。通过改进K-Means算法，Pearson距离从96％到98.9％提高二元协议聚类的准确性。 DCBP算法有助于我们准确地确定k值。本文确定的k值为5，聚类精度为98.9％。当K为6时，聚类精度为80％，当K为6时，k为6.并且改进的K-means算法的操作速度优于Agnes算法的操作速度。该算法更好地适应未知二进制协议的聚类，并提高了聚类的准确性和操作速度。

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