信息安全评估日志数据的一种混合聚类算法

A Hybrid Clustering Algorithm for Information Security Evaluation Log Data

首先引入能够处理混合型数据的K-prototypes聚类算法,在此基础上构造了一种基于粒子群优化算法和K-prototypes方法的混合聚类算法.利用粒子群优化算法良好的全局搜索能力,克服K-prototypes容易陷入局部最优值的不足.实验结果表明,该算法能够避免陷入局部最优值,具有较好的全局收敛性,并且提高了聚类的正确率和算法的稳定性.

There are many categorical and numericalattributes in source data of information security evaluation. When dealing with mixed data, the traditional cluster algorithms tend to converge to a local optimum or can only process numerical data. In order to solve these problems, this paper employs the K-prototypes cluster algorithm to deal with mixed numerical data, and a hybrid cluster algorithm is designed based on particle swarm optimization algorithm and K-prototypes algorithm, combing the K-prototypes algorithm＇ s ability on local search and fast convergence and the particle swarm optimization algorithm＇ s ability on global search. Experimental results show that the hybrid algorithm has better performance, strong robustness and fast convergence speed.