Clustering of large scale QoS time series data in federated clouds using improved variable Chromosome Length Genetic Algorithm (CQGA)
作者：
Highlights：
• Shape based clustering of large scale QoS time series data in federated clouds.
• Genetic based clustering using variable chromosome length.
• Crossover and Mutation operation using the meta data of time series.
• Simultaneous lookup of the best number of clusters while clustering.
• Accurate and faster clustering performance compared to state-of-the-art methods.
摘要
•Shape based clustering of large scale QoS time series data in federated clouds.•Genetic based clustering using variable chromosome length.•Crossover and Mutation operation using the meta data of time series.•Simultaneous lookup of the best number of clusters while clustering.•Accurate and faster clustering performance compared to state-of-the-art methods.
论文关键词：Time series Clustering,Genetic algorithm,Quality of Service,Large data set,Dynamic Time Warping,Variable chromosome length
论文评审过程：Received 8 September 2019, Revised 22 May 2020, Accepted 1 August 2020, Available online 7 August 2020, Version of Record 10 August 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113840
