文档介绍：第 5 期 组合机床与自动化加工技术 No． 5
2022 年 525 China
�
Abstract In order to solve the problem that the accuracy of traditional clustering algorithm applied directly
，
to time series clustering in industrial production is low a K-medoids algorithm based on DTW distance
measurement is proposed． DTW is used to calculate the distance between time series data instead of the tra-
，
ditional Euclidean distance measurement which improves the accuracy of similarity measurement algorithm
，
and the accuracy of clustering algorithm and realizes the supervision and anomaly detection of time series
，
data by building a threshold mechanism． Finally combining with the time series data of tobacco moisture
， ，
content and comparing with the anomaly detection model of traditional clustering algorithm the experimen-
tal results show that the DTW-k