Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data
Abstract
This paper presents a comprehensive comparative analysis of prominent clustering algorithms K-means, DBSCAN, and Spectral Clustering on high-dimensional datasets. We introduce a novel evaluation framework that assesses clustering performance across multiple dimensionality reduction techniques (PCA, t-SNE, and UMAP) using diverse quantitative metrics. Experiments conducted on MNIST, Fashion-MNIST, and UCI HAR datasets reveal that preprocessing with UMAP consistently improves clustering quality across all algorithms, with Spectral Clustering demonstrating superior performance on complex manifold structures. Our findings show that algorithm selection should be guided by data characteristics, with Kmeans excelling in computational efficiency, DBSCAN in handling irregular clusters, and Spectral Clustering in capturing complex relationships. This research contributes a systematic approach for evaluating and selecting clustering techniques for high dimensional data applications.
Cite
@article{arxiv.2503.23215,
title = {Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data},
author = {Vishnu Vardhan Baligodugula and Fathi Amsaad},
journal= {arXiv preprint arXiv:2503.23215},
year = {2025}
}
Comments
The paper is being withdrawn due to significant errors in the analysis that affect the validity of the conclusions. A revised version may be submitted in the future once the issues are resolved