Brain image segmentation using semi-supervised clustering
作者：
Highlights：
• Here multiple centers are used to represent a partitioning.
• We have assumed that for 10% data points actual class labels are known.
• Used objective functions are based on internal and external cluster validity indices.
• AMOSA is used to optimize these three objective functions.
• Three different mutation operators are used to obtain global Pareto front.
摘要
•Here multiple centers are used to represent a partitioning.•We have assumed that for 10% data points actual class labels are known.•Used objective functions are based on internal and external cluster validity indices.•AMOSA is used to optimize these three objective functions.•Three different mutation operators are used to obtain global Pareto front.
论文关键词：Brain image segmentation,Multiobjective optimization,Semi-supervised clustering,AMOSA,Cluster validity index,Sym-index,I-index,MS-index
论文评审过程：Received 6 June 2015, Revised 5 January 2016, Accepted 6 January 2016, Available online 13 January 2016, Version of Record 2 February 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.01.005
