Gaussian kernel c-means hard clustering algorithms with automated computation of the width hyper-parameters
作者：
Highlights：
• The paper provides c-means algorithms based on Gaussian kernel functions.
• The algorithms are able to compute iteratively the width hyper-parameters.
• The algorithms are able to select the relevant variables for the clustering task.
• Experiments with synthetic and real data sets shows the usefulness of the algorithms.
摘要
•The paper provides c-means algorithms based on Gaussian kernel functions.•The algorithms are able to compute iteratively the width hyper-parameters.•The algorithms are able to select the relevant variables for the clustering task.•Experiments with synthetic and real data sets shows the usefulness of the algorithms.
论文关键词：Gaussian kernel clustering,Kernelization of the metric,Feature space,Width hyper-parameter
论文评审过程：Received 26 December 2016, Revised 18 January 2018, Accepted 18 February 2018, Available online 19 February 2018, Version of Record 27 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.02.018
