Optimized k-means clustering algorithm based on artificial fish swarm

机译：基于人工鱼群的优化k均值聚类算法

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To improve the situation of insufficient global research in K-Means Algorithm, this paper discusses an optimized K-Means clustering algorithm based on artificial fish swarm, which overcomes the sensitivity to initial clustering center selection of K-Means clustering algorithm and gets the optimal global clustering partition. Meanwhile, to improve the precision of clustering algorithm, a novel algorithm is presented to calculate the inner-class distance and inter-class distance. Simulation experiments have been implemented over data set KDD-99, and the results showed that the satisfactory detection rate and false acceptance rate could be obtained in network intrusion detection.

机译：为了改善K-Means算法全局研究不足的情况，本文讨论了一种基于人工鱼群的K-Means优化聚类算法，克服了K-Means聚类算法对初始聚类中心选择的敏感性，获得了最优的全局集群分区。同时，为提高聚类算法的精度，提出了一种新的算法来计算内部类距离和类间距离。在数据集KDD-99上进行了仿真实验，结果表明，在网络入侵检测中可以获得令人满意的检测率和错误接受率。

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来源
《Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer》|2013年|1783-1787|共5页
会议地点 Shenyang(CN)
作者
HaiTao Yu; Meijuan Jia; Xiaoxu Cheng; Qingfeng Jiang;
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作者单位

College of computer science and information technology, Daqing Normal University, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Artificial Fish Swarm; Clustering; Intrusion detection; inner-class distance; inter-class distance;

机译：人工鱼群；集群；入侵检测;内在距离类间距离；;

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4. Optimized k-means clustering algorithm based on artificial fish swarm [C] . HaiTao Yu, Meijuan Jia, Xiaoxu Cheng, International Conference on Mechatronic Sciences, Electric Engineering and Computer . 2013

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5. A comparison of particle swarm optimization algorithms in data clustering. [D] . Chung, Yu-Kuang. 2010

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7. Mixed clustering algorithm with artificial fish swarm and improved K-means [O] . Hao Yang 2016

机译：用人工鱼类群和改进的K均值混合聚类算法

Optimized k-means clustering algorithm based on artificial fish swarm

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