Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering  

作　　者：Rui Wang Wenhua Li Kaili Shen Tao Zhang Xiangke Liao 

机构地区：[1]Xiangjiang Laboratory,Changsha 410205,China [2]College of Systems Engineering,National University of Defense Technology,Changsha 410073,China [3]Ant Group Co.,Ltd.,Hangzhou 310000,China [4]College of Computer Science and Technology,NUDT,Changsha 410073,China

出　　处：《Tsinghua Science and Technology》2024年第2期343-355,共13页清华大学学报（自然科学版（英文版）

基　　金：supported by the Open Project of Xiangjiang Laboratory(No.22XJ02003);the National Natural Science Foundation of China(No.62122093).

摘　　要：Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series data.Traditional clustering methods often use a single criterion or distance measure,which may not capture all the features of the data.This paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance measure.Therefore,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of decision-makers.Experiments on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering quality.The paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.

关 键 词：time series clustering evolutionary multi-tasking multifactorial optimization clustering validity index distance measure 

分 类 号：Q811.4[生物学—生物工程]