# Recovering the number of clusters in data sets with noise features using feature rescaling factors

arXiv: https://arxiv.org/abs/1602.06989 · HTML: https://mlsanity.com/paper/1602.06989/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2016.

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

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- [Detecting Biological Locomotion in Video: A Computational Approach](https://mlsanity.com/paper/2105.12661/) (2021)
- [Leveraging Structure for Improved Classification of Grouped Biased Data](https://mlsanity.com/paper/2212.03697/) (2022)
- [Datacube segmentation via Deep Spectral Clustering](https://mlsanity.com/paper/2401.17695/) (2024)
- [Number of Clusters in a Dataset: A Regularized K-means Approach](https://mlsanity.com/paper/2505.22991/) (2025)
- [Cluster-Adaptive Feature Extraction and its Theoretical Foundation with Minkowski Weighted k-Means](https://mlsanity.com/paper/2603.25958/) (2026)

## Abstract (arXiv)

In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set and the intuitive idea that different features may have different degrees of relevance at different clusters. We experiment with the Silhouette (using squared Euclidean, Manhattan, and the pth power of the Minkowski distance), Dunn's, Calinski-Harabasz and Hartigan indexes on data sets with spherical Gaussian clusters with and without noise features. We conclude that our methods indeed increase the chances of estimating the true number of clusters in a data set.