聚类分析
层次聚类
计算机科学
等级制度
单连锁聚类
星团(航天器)
数据挖掘
全球定位系统
集合(抽象数据类型)
趋同(经济学)
k-中位数聚类
相关聚类
CURE数据聚类算法
地理
人工智能
计算机网络
市场经济
电信
经济
经济增长
程序设计语言
作者
Cristóbal Heredia,Sebastián Moreno,Wilfredo F. Yushimito
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-10-06
卷期号:23 (8): 12700-12710
标识
DOI:10.1109/tits.2021.3116963
摘要
Clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms. In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city due to its hierarchical structure.
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