A Survey on Soft Subspace Clustering
It organizes existing knowledge on SSC, an emerging area in clustering for high-dimensional data, but is incremental as a survey.
This paper provides a comprehensive survey of soft subspace clustering (SSC) algorithms, categorizing them into three main types and discussing their characteristics and future developments.
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.