Clustering by centroid drift and boundary shrinkage

作者：

Highlights：

• We propose a three-stage clustering framework based on the pre-computed k nearest neighbors matrix.

• A CD metric is developed in this paper to quantify the possibility of each data point as a boundary point.

• The BS strategy completes the allocation of labels by shrinking the boundary points inwards.

摘要

•We propose a three-stage clustering framework based on the pre-computed k nearest neighbors matrix.•A CD metric is developed in this paper to quantify the possibility of each data point as a boundary point.•The BS strategy completes the allocation of labels by shrinking the boundary points inwards.

论文关键词：Clustering,Centroid drift,Boundary detection

论文评审过程：Received 19 April 2021, Revised 6 April 2022, Accepted 24 April 2022, Available online 26 April 2022, Version of Record 1 May 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108745