Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data
This provides a systematic evaluation framework for practitioners working with high-dimensional clustering problems, though it is incremental in nature.
This paper tackled the problem of selecting clustering algorithms for high-dimensional data by comparing K-means, DBSCAN, and Spectral Clustering across multiple datasets and dimensionality reduction techniques. The results showed that UMAP preprocessing consistently improved clustering quality, with Spectral Clustering performing best on complex manifold structures.
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.