# Shapley-Inspired Feature Weighting in k-means with No Additional Hyperparameters

arXiv: https://arxiv.org/abs/2508.07952 · HTML: https://mlsanity.com/paper/2508.07952/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Explaining dimensionality reduction results using Shapley values](https://mlsanity.com/paper/2103.05678/) (2021)
- [A Survey on Soft Subspace Clustering](https://mlsanity.com/paper/1409.5616/) (2014)
- [Identifying meaningful clusters in malware data](https://mlsanity.com/paper/2008.01175/) (2020)

## Built on by (most cited first, 1 of 1)

- [Counterfactuals for Feature-Weighted Clustering](https://mlsanity.com/paper/2607.14719/) (2026)

## Abstract (arXiv)

Clustering algorithms often assume all features contribute equally to the data structure, an assumption that usually fails in high-dimensional or noisy settings. Feature weighting methods can address this, but most require additional parameter tuning. We propose SHARK (Shapley Reweighted k-means), a feature-weighted clustering algorithm motivated by the use of Shapley values from cooperative game theory to quantify feature relevance, which requires no additional parameters beyond those in k-means. We prove that the k-means objective can be decomposed into a sum of per-feature Shapley values, providing an axiomatic foundation for unsupervised feature relevance and reducing Shapley computation from exponential to polynomial time. SHARK iteratively re-weights features by the inverse of their Shapley contribution, emphasising informative dimensions and down-weighting irrelevant ones, and is equivalent to replacing the arithmetic mean of feature dispersions with their harmonic mean. Experiments on synthetic and real-world data sets show that SHARK consistently matches or outperforms existing methods, achieving superior robustness and accuracy, particularly in scenarios where noise may be present. Software: https://github.com/rickfawley/SHARK.