Kernel-based hard clustering methods in the feature space with automatic variable weighting

作者：

Highlights：

• The paper gives kernel-based hard clustering algorithms in the feature space.

• The algorithms learn a relevance weight for each variable.

• Partition and cluster interpretation tools are given.

• Applications on synthetic and real datasets corroborate the proposed algorithms.

摘要

Highlights•The paper gives kernel-based hard clustering algorithms in the feature space.•The algorithms learn a relevance weight for each variable.•Partition and cluster interpretation tools are given.•Applications on synthetic and real datasets corroborate the proposed algorithms.

论文关键词：Kernel clustering,Feature space,Adaptive distances,Clustering analysis

论文评审过程：Received 25 March 2013, Revised 31 January 2014, Accepted 26 March 2014, Available online 4 April 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.03.026