# Assessing the impact of dimensionality reduction on clustering performance - a systematic study

arXiv: https://arxiv.org/abs/2604.22099 · HTML: https://mlsanity.com/paper/2604.22099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Clustering Algorithms: A Comparative Approach](https://mlsanity.com/paper/1612.08388/) (2016)
- [Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study](https://mlsanity.com/paper/2110.02894/) (2021)
- [Statistical power for cluster analysis](https://mlsanity.com/paper/2003.00381/) (2020)
- [Enhancing cluster analysis via topological manifold learning](https://mlsanity.com/paper/2207.00510/) (2022)
- [Improving clustering quality evaluation in noisy Gaussian mixtures](https://mlsanity.com/paper/2503.00379/) (2025)
- [Natural Language-Based Synthetic Data Generation for Cluster Analysis](https://mlsanity.com/paper/2303.14301/) (2023)

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

- [TopicMerge: A Divide and Conquer Approach to Topic Modeling](https://mlsanity.com/paper/2610.19323/) (2026)

## Abstract (arXiv)

Dimensionality reduction is a critical preprocessing step for clustering high-dimensional data, yet comprehensive evaluation of its impact across diverse methods and data types remains limited. In this study, we systematically assess the influence of five dimensionality reduction techniques - Principal Component Analysis (PCA), Kernel Principal Component Analysis (Kernel PCA), Variational Autoencoder (VAE), Isometric Mapping (Isomap), and Multidimensional Scaling (MDS) - on the performance of four popular clustering algorithms - k-means, Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Models (GMM), and Ordering Points to Identify the Clustering Structure (OPTICS). We evaluate clustering quality using the Adjusted Rand Index (ARI), comparing results without and with dimensionality reduction at different reduction levels recommended in the literature (i.e., k-1, where k is the number of clusters, and 25% and 50% of the original number of dimensions). Our findings underscore the importance of a careful selection of the dimensionality reduction technique and the dimensionality reduction level that should be tailored to intrinsic data geometry and clustering algorithm(s) under consideration.