Detecting change-points for shifts in mean and variance using fuzzy classification maximum likelihood change-point algorithms
作者：
Highlights：
• We propose a new algorithm, FCML-CP, to detect change-points in a statistical process.
• The mixture likelihood embedded fuzzy c-partition is utilized to estimate the change-points.
• The method outperforms statistical likelihood approaches.
• Various experiments show the effectiveness and practicability of the proposed method.
• Excellent performance in detecting small changes is helpful for root cause analyses.
摘要
•We propose a new algorithm, FCML-CP, to detect change-points in a statistical process.•The mixture likelihood embedded fuzzy c-partition is utilized to estimate the change-points.•The method outperforms statistical likelihood approaches.•Various experiments show the effectiveness and practicability of the proposed method.•Excellent performance in detecting small changes is helpful for root cause analyses.
论文关键词：Control chart,Change-point,Mixture model,Fuzzy clustering,Fuzzy classification maximum likelihood change-point algorithm
论文评审过程：Received 9 November 2015, Revised 22 May 2016, Available online 11 June 2016, Version of Record 19 July 2016.
论文官网地址：https://doi.org/10.1016/j.cam.2016.06.006
