Data clustering using proximity matrices with missing values

作者：

Highlights：

• There is a need for methods for dealing with missing data in proximity matrices.

• New method called Proximity Matrix Completion algorithm addresses this need.

• The PMC algorithm is shown to be effective compared to benchmarks.

• A case study from plant breeding established practical relevance.

摘要

•There is a need for methods for dealing with missing data in proximity matrices.•New method called Proximity Matrix Completion algorithm addresses this need.•The PMC algorithm is shown to be effective compared to benchmarks.•A case study from plant breeding established practical relevance.

论文关键词：Clustering,Imputation,Missing values,Proximity matrix

论文评审过程：Received 9 December 2018, Revised 16 February 2019, Accepted 17 February 2019, Available online 21 February 2019, Version of Record 1 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.02.022