基于航迹数据的改进DBSCAN聚类算法研究OA
Research on improved DBSCAN clustering algorithm based on track data
为研究模拟训练航迹数据聚类,针对基于密度的噪声应用空间聚类(DBSCAN)算法参数选取不精准、聚类准确度不高的问题,提出一种改进的DBSCAN聚类算法.首先,通过KNN算法计算邻域半径并得到用于DBSCAN聚类的初始化核心数据对象,实现粗聚类;其次,根据数据对象的特点,加入航向特征进行二次聚类,既解决了DBSCAN算法随机初始化核心点和参数选取难的问题,又加入能够反映数据方向的特征;最后,进行了仿真实验.实验结果表明,改进DBSCAN算法比传统DBSCAN算法具有更好的聚类效果.
In order to study the simulation training track data clustering,this paper aims at the problem of in-accurate parameter selection and low clustering accuracy of the traditional DBSCAN algorithm to propose an im-proved DBSCAN clustering algorithm.Firstly,KNN algorithm is used to calculate the neighborhood radius and obtain the initializing core data object for DBSCAN clustering to realize rough clustering.Then,according to the characteristics of data objects,heading features are added for a secondary clustering,which not only solves the dif-ficulty of randomly initialized core point and parameter selection of DBSCAN algorithm,but also adds features that reflect the direction of the data.Finally,a simulation experiment is carried out.The experimental results show that the improved DBSCAN algorithm has better clustering effect than the traditional algorithm.
申正义;李平;王洪林;赵迪;郭文琪
空军预警学院,武汉 43001931435部队,沈阳 110015
电子信息工程
模拟训练DBSCAN算法二次聚类自适应参数选取航迹数据
simulation trainingDBSCAN algorithmsecondary clusteringadaptive parameter selectiontrack data
《空天预警研究学报》 2024 (002)
128-131 / 4
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