A study of divisive clustering with Hausdorff distances for interval data
作者：
Highlights：
• Hausdorrf, Gowda–Diday and Ichino–Yaguchi distances for intervals are compared.
• Euclidean counterparts and their normalizations are included.
• Summary of advantages and disadvantages of these respective distances are based on simulation studies.
• The simulation study shows local normalizations outperform global normalizations.
摘要
•Hausdorrf, Gowda–Diday and Ichino–Yaguchi distances for intervals are compared.•Euclidean counterparts and their normalizations are included.•Summary of advantages and disadvantages of these respective distances are based on simulation studies.•The simulation study shows local normalizations outperform global normalizations.
论文关键词：Interval data,Divisive clustering,Hausdorff distances,Gowda–Diday distances,Ichino–Yaguchi distances,Span normalization,Euclidean normalization,Local and global normalizations
论文评审过程：Received 19 February 2019, Revised 30 April 2019, Accepted 15 July 2019, Available online 24 July 2019, Version of Record 2 September 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.106969
