基于聚类的目标行为异常检测
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摘要
聚类是一种无指导的学习过程,无需先验知识即可完成特征分类。在层次化聚类算法基础上,介绍聚类方法对异常行为检测理论方法。对目标样本数据特征分析,建立了目标运动特征异常的检测工程模型。基于累积数据聚类生成了特定区域目标运动特征知识库,对实时数据测试分析,计算得到了异常目标集合。异常目标运动特征数据可视化,验证了目标运动特征异常检测模型的准确性与可实现性。
Clustering is an unsupervised learning process, which can complete feature classification without prior knowledge. On the basis of hierarchical clustering algorithm, this paper introduces the theory and method of clustering to detect abnormal behavior. Based on the analysis of target sample data characteristics, an engineering model for anomaly detection of the target motion characteristics is established. Based on accumulated data clustering, a knowledge base of target motion characteristics in specific regions is generated.The real-time data are tested, analyzed and calculated to obtain abnormal target set. The abnormal target motion feature data is visualized to verify the accuracy and feasibility of the anomaly detection model.
Clustering is an unsupervised learning process, which can complete feature classification without prior knowledge. On the basis of hierarchical clustering algorithm, this paper introduces the theory and method of clustering to detect abnormal behavior. Based on the analysis of target sample data characteristics, an engineering model for anomaly detection of the target motion characteristics is established. Based on accumulated data clustering, a knowledge base of target motion characteristics in specific regions is generated.The real-time data are tested, analyzed and calculated to obtain abnormal target set. The abnormal target motion feature data is visualized to verify the accuracy and feasibility of the anomaly detection model.
引文
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[2]陈勇.一种目标航迹数据聚类挖掘分析方法[J].无线电工程,2015,45(3):22-24.
[3]白洁,田瑞丽,张学军.Apriori算法在用户特性关联分析中的应用[J].计算机与网络,2016,42(12):70-72.
[4]Dunham M H.数据挖掘教程[M].郭崇慧,田凤占等,译.北京:清华大学出版社,2005.
[5]Tan P N,Steinbach M,Kumar V.数据挖掘导论[M].范明,范宏建等,译.北京:人民邮电出版社,2006.