Extreme point bias compensation: A similarity method of functional clustering and its application to the stock market

作者：

Highlights：

• This paper presents a novel approach for functional clustering.

• Numerical distance approach and a curve shape approach are combined.

• The comparative analysis confirms superiority of the proposed method.

• Illustrative example for stock data is provided.

摘要

•This paper presents a novel approach for functional clustering.•Numerical distance approach and a curve shape approach are combined.•The comparative analysis confirms superiority of the proposed method.•Illustrative example for stock data is provided.

论文关键词：Functional clustering,Bias compensation,Extreme point,Similarity measure,Curve shape

论文评审过程：Received 4 August 2019, Revised 29 August 2020, Accepted 29 August 2020, Available online 7 September 2020, Version of Record 18 September 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113949