计算机与数字工程2024，Vol.52Issue(6)：1630-1635,1649,7.DOI:10.3969/j.issn.1672-9722.2024.06.007
基于马氏距离和Canopy改进K-means的交通聚类算法
Traffic Clustering Algorithm Based on Markov Distance and Canopy Improved K-means
摘要
Abstract
Clustering algorithms are often used in the research of traffic data,and different clustering algorithms have differ-ent characteristics.As one of the clustering algorithms,K-means has high accuracy and practicability,but its accuracy is easily af-fected by subjective selection of K value and determination of initial clustering center.In order to optimize the selection of clustering center and K value,MC-Kmeans algorithm is proposed In the proposed method,firstly,the K value is selected by canopy algo-rithm,and then the initial cluster center is determined according to the calculation criterion of Mahalanobis distance.Finally,the K value and the value of cluster center are clustered as the parameters of K-means MC-Kmeans algorithm is applied to New York taxi traffic data in a certain period of time for practical verification.The results show that compared with K-means algorithm,the pro-posed method has higher accuracy,better matches the actual traffic situation,and can better reflect the traffic hot spots in the re-gion.关键词
K-means/Canopy算法/马氏距离/交通Key words
K-means/Canopy algorithm/Markov distance/traffic分类
信息技术与安全科学引用本文复制引用
徐文进,马越,杜咏慧..基于马氏距离和Canopy改进K-means的交通聚类算法[J].计算机与数字工程,2024,52(6):1630-1635,1649,7.基金项目
山东省自然科学基金项目(编号:2018GGX105005)资助. （编号:2018GGX105005）
