A Survey on Soft Subspace Clustering

TL;DR

This survey reviews the development of soft subspace clustering algorithms, categorizing them into three main types, and discusses their characteristics and future research directions in high-dimensional data analysis.

Contribution

It provides a comprehensive classification and analysis of existing SSC algorithms, highlighting their features and potential future developments.

Findings

SSC algorithms are gaining attention due to better adaptability.

Classification into CSSC, ISSC, and XSSC clarifies the landscape.

Future research directions are discussed.

Abstract

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been extensively studied and well accepted by the scientific community, SSC algorithms are relatively new but gaining more attention in recent years due to better adaptability. In the paper, a comprehensive survey on existing SSC algorithms and the recent development are presented. The SSC algorithms are classified systematically into three main categories, namely, conventional SSC (CSSC), independent SSC (ISSC) and extended SSC (XSSC). The characteristics of these algorithms are highlighted and the potential future development of SSC is also discussed.