ISBFK-means: A new clustering algorithm based on influence space

作者：

Highlights：

• The impact of outliers on clustering results is reduced using the influence space.

• Representative data objects of the original datasets are extracted under the influence space.

• A clustering algorithm called ISBFK-means is proposed based on influence space.

• Valuable information hidden in LAMOST low-quality spectra is revealed by ISBFK-means algorithm.

摘要

•The impact of outliers on clustering results is reduced using the influence space.•Representative data objects of the original datasets are extracted under the influence space.•A clustering algorithm called ISBFK-means is proposed based on influence space.•Valuable information hidden in LAMOST low-quality spectra is revealed by ISBFK-means algorithm.

论文关键词：Clustering,Influence space,Region partition,Representative data objects

论文评审过程：Received 10 August 2021, Revised 22 January 2022, Accepted 27 March 2022, Available online 7 April 2022, Version of Record 18 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117018