Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems  

作　　者：Caizhi Zhang Weifeng Huang Tong Niu Zhitao Liu Guofa Li Dongpu Cao 

机构地区：[1]Chongqing Automotive Collaborative Innovation Centre,The State Key Laboratory of Mechanical Transmissions,Chongqing University,Chongqing 400044,China [2]State Key Laboratory of Industrial Control Technology,Institute of Cyber‑Systems and Control,Zhejiang University,Hangzhou 310027,China [3]School of Vehicle and Mobility,Tsinghua University,Beijing,China

出　　处：《Automotive Innovation》2023年第1期89-115,共27页汽车创新工程（英文）

基　　金：supported in part by the founding of the State Key Laboratory of Industrial Control Technology,Zhejiang University(ICT2021B19);the Technological Innovation and Application Demonstration in Chongqing(Major Themes of Industry:cstc2019jscx-zdztzxX0033,cstc2019jscx-fxyd0158).

摘　　要：Clustering is an unsupervised learning technology,and it groups information(observations or datasets)according to similarity measures.Developing clustering algorithms is a hot topic in recent years,and this area develops rapidly with the increasing complexity of data and the volume of datasets.In this paper,the concept of clustering is introduced,and the clustering technologies are analyzed from traditional and modern perspectives.First,this paper summarizes the principles,advantages,and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms.Then,the core elements of clustering are presented,such as similarity measures and evaluation index.Considering that data processing is often applied in vehicle engineering,finally,some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted.The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use,especially in vehicles.

关 键 词：Unsupervised learning CLUSTERING Similarity measures VEHICLE 

分 类 号：U46[机械工程—车辆工程]