空天预警研究学报2024，Vol.38Issue(2)：128-131,4.DOI:10.3969/j.issn.2097-180X.2024.02.011
基于航迹数据的改进DBSCAN聚类算法研究
Research on improved DBSCAN clustering algorithm based on track data
申正义 1李平 1王洪林 1赵迪 2郭文琪1
作者信息
- 1. 空军预警学院,武汉 430019
- 2. 31435部队,沈阳 110015
- 折叠
摘要
Abstract
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.关键词
模拟训练/DBSCAN算法/二次聚类/自适应参数选取/航迹数据Key words
simulation training/DBSCAN algorithm/secondary clustering/adaptive parameter selection/track data分类
信息技术与安全科学引用本文复制引用
申正义,李平,王洪林,赵迪,郭文琪..基于航迹数据的改进DBSCAN聚类算法研究[J].空天预警研究学报,2024,38(2):128-131,4.
