Search for evergreens in science: A functional data analysis
作者：
Highlights：
• We introduce functional data analysis to bibliometric studies.
• We develop a clustering approach by combining functional principal component analysis, Poisson regression, and K-means clustering.
• We identify a cluster of evergreen papers, which do not exhibit any decline in annual citations over 30 years.
• Evergreens have important implications for modeling citation dynamics and research evaluations.
摘要
•We introduce functional data analysis to bibliometric studies.•We develop a clustering approach by combining functional principal component analysis, Poisson regression, and K-means clustering.•We identify a cluster of evergreen papers, which do not exhibit any decline in annual citations over 30 years.•Evergreens have important implications for modeling citation dynamics and research evaluations.
论文关键词：Citation trajectory,Evergreen,Functional Poisson regression,Functional principal component analysis,K-means clustering
论文评审过程：Received 6 December 2016, Revised 17 May 2017, Accepted 17 May 2017, Available online 13 June 2017, Version of Record 13 June 2017.
论文官网地址：https://doi.org/10.1016/j.joi.2017.05.007
