Projected fuzzy C-means clustering with locality preservation

作者：

Highlights：

• A novel locality preserving based fuzzy C-means clustering method (LPFCM) is presented.

• An orthogonally projected space, which preserves the locality of structural properties, can be generated in LPFCM.

• The capability of FCM for handling high-dimensional data can be enhanced.

• The ideas of fuzzy clustering, geometric structure preservation and feature extraction are seamlessly integrated.

• Experimental results on some benchmark data sets show the effectiveness of LPFCM.

摘要

•A novel locality preserving based fuzzy C-means clustering method (LPFCM) is presented.•An orthogonally projected space, which preserves the locality of structural properties, can be generated in LPFCM.•The capability of FCM for handling high-dimensional data can be enhanced.•The ideas of fuzzy clustering, geometric structure preservation and feature extraction are seamlessly integrated.•Experimental results on some benchmark data sets show the effectiveness of LPFCM.

论文关键词：Fuzzy C-means,Locality preserving projections,Clustering,Projection-based spatial transformation

论文评审过程：Received 26 May 2020, Revised 29 August 2020, Accepted 1 November 2020, Available online 2 November 2020, Version of Record 19 February 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107748