Projected fuzzy C-means clustering with locality preservation. (May 2021)

• Record Type: Journal Article
• Authors: Zhou, Jie; Pedrycz, Witold; Yue, Xiaodong; Gao, Can; Lai, Zhihui; Wan, Jun
• Is Part Of: Pattern recognition. Volume 113(2021)
• Publication Date: 2021-05
• Subjects: Fuzzy C-means -- Locality preserving projections -- Clustering -- Projection-based spatial transformation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107748
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract: Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not deal with high-dimensional data sets effectively as redundant features may impact the computation of distances and local spatial structures among patterns are rarely considered. High dimensionality of space gives rise to so-called concentration effect that is detrimental. In this paper, a novel locality preserving based fuzzy C-means (LPFCM) clustering method and its optimization are presented. An orthogonally projected space, which preserves the locality of structural properties, can be generated in LPFCM, thus enhancing the capability of fuzzy C-means (FCM) for handling high-dimensional data. It is the first time to introduce projection techniques to the FCM optimization objective function directly, and the ideas of fuzzy clustering, geometric structure preservation and feature extraction are seamlessly integrated. LPFCM is also regarded as a unified model that combines two separate stages of spectral clustering. Experimental results on some benchmark data sets show the effectiveness of LPFCM in comparison with FCM and some state-of-the-art methods.