# A Survey on Soft Subspace Clustering

arXiv: https://arxiv.org/abs/1409.5616 · HTML: https://mlsanity.com/paper/1409.5616/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2014.

## Built on by (most cited first, 7 of 7)

- [Shapley-Inspired Feature Weighting in k-means with No Additional Hyperparameters](https://mlsanity.com/paper/2508.07952/) (2025)
- [A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles](https://mlsanity.com/paper/2101.04932/) (2021)
- [Fuzzy clustering algorithms with distance metric learning and entropy regularization](https://mlsanity.com/paper/2102.09529/) (2021)
- [A Self-Constructing Multi-Expert Fuzzy System for High-dimensional Data Classification](https://mlsanity.com/paper/2410.13390/) (2024)
- [Scalable unsupervised feature selection via weight stability](https://mlsanity.com/paper/2506.06114/) (2025)
- [Cluster-Adaptive Feature Extraction and its Theoretical Foundation with Minkowski Weighted k-Means](https://mlsanity.com/paper/2603.25958/) (2026)
- [Counterfactuals for Feature-Weighted Clustering](https://mlsanity.com/paper/2607.14719/) (2026)

## Abstract (arXiv)

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.