Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data (2503.23215v1)

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.

• The paper presents a novel framework for comparatively analyzing K-means, DBSCAN, and Spectral Clustering performance on high-dimensional data, integrating dimensionality reduction techniques.
• Key findings show dimensionality reduction, especially UMAP, significantly improves outcomes; Spectral Clustering excels on complex data like MNIST (ARI 0.794); K-means is fastest; and DBSCAN is best for noisy data.
• Practical recommendations guide algorithm selection based on data structure and resource constraints, advising UMAP for preprocessing, K-means for speed, DBSCAN for noise, and Spectral Clustering for performance on intricate datasets.

Comparative Analysis of Clustering Techniques on High-Dimensional Data

The paper "Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data" presents a thorough examination of clustering algorithms in high-dimensional settings, demonstrating a novel framework for evaluating the performance of three widely-used algorithms: K-means, DBSCAN, and Spectral Clustering. By integrating multiple dimensionality reduction techniques—PCA, t-SNE, and UMAP—and employing comprehensive evaluation metrics, this paper provides substantive insights into algorithmic efficiencies within the context of high-dimensional data.

Key Findings

The research establishes a framework for systematically comparing clustering algorithms, emphasizing the interaction between dimensionality reduction techniques and clustering performance. Central findings suggest preprocessors like UMAP significantly enhance clustering outcomes. Spectral Clustering emerged as the superior performer on datasets with intricate manifold structures, notably showing an ARI of 0.794 on MNIST post-UMAP reduction. K-means claims computational efficiency, performing 15-50 times faster than other algorithms. Meanwhile, DBSCAN excels in scenarios demanding robust noise handling.

Empirically, the paper tested performance across various datasets: MNIST, Fashion-MNIST, and UCI HAR. Results consistently demonstrated that dimensionality reduction is a critical preprocessing step, enhancing each algorithm's performance. UMAP's ability to maintain structure at various scales proved advantageous over PCA and t-SNE.

Implications and Practical Recommendations

The paper provides valuable insights for practitioners in selecting suitable clustering methodologies based on data characteristics. Practical recommendations include adopting UMAP for dimensionality reduction when CPU and memory resources allow. K-means remains advisable for environments valuing speed, while DBSCAN's utility is pronounced in irregular or noisy datasets. Spectral Clustering's proficiency in complex data suggests its use when performance is prioritized over computational speed.

Methodological Contributions

Beyond empirical findings, this work introduces a robust evaluation framework integrating internal (Silhouette, Davies-Bouldin) and external metrics (ARI, NMI), alongside computational considerations like runtime and memory usage. It advocates applying diverse metrics to capture varied dimensions of clustering quality, especially when ground truths are unavailable.

Limitations and Future Research

Despite its contributions, the paper is constrained by computational scalability issues, highlighting the challenges posed by very large datasets. Future work should extend with broad algorithmic coverage, including hierarchical methods and emerging deep clustering techniques. The exploration of advanced ensemble methods could harness the strengths of individual algorithms to improve overall cluster discovery. Furthermore, efforts toward automated parameter selection could democratize clustering methods, allowing broad applicability without substantial domain expertise.

In summary, this paper offers a comprehensive analysis of clustering strategies in high-dimensional contexts, presenting substantial evidence and practical guidance for data preprocessing and algorithm selection, while highlighting areas ripe for future exploration in clustering methodologies.