Density-Peak-Based Clustering in Reduced Feature Spaces

Abstract

Clustering is a fundamental data mining technique that groups data points by similarity. A critical challenge for clustering algorithms is the effective selection of initial cluster centers, often done through inefficient trial-and-error. To address this, a novel Adaptive Cluster Center Initialization using Density Peak for Geodesic Distance-based Clustering (AGDPC) method intelligently identifies optimal centers. It builds upon the Density Peaks Clustering (DPC) and Geodesic-Based Initialization (GDPC) approaches, using weighted Euclidean distances that incorporate the Pearson correlation coefficient. Furthermore, AGDPC adaptively optimizes its threshold parameter using data field density estimation entropy, enhancing its accuracy and automation. It should be noted that, AGDPC currently operates directly on high-dimensional data without dimensionality reduction. While this preserves the original structure of the data, high-dimensional spaces often suffer from the “curse of dimensionality,” where distances between points become less meaningful, and computational complexity increases. Introducing Multidimensional Scaling (MDS) as a pre-processing step can address these challenges by projecting data into a lower-dimensional space while preserving pairwise distances or dissimilarities as much as possible. In this work, we propose to use multidimensional scaling that mitigates the impact of irrelevant or redundant features, enabling 2D/3D visualisation of clusters for interpretability. We proposed to use Regularised Multidimensional Scaling using Radial Basis Function (RBF-MDS) for dimension reduction. The idea is to decrease its dimensionality, followed by the application of the adaptive strategy on the dataset in its reduced dimension. Our approaches are tested on various benchmarking datasets, and the outcomes are contrasted with those of DPC, GDPC, and one of the most conventional clustering methods, K-Means clustering. When tested on actual data, the experimental findings show that the proposed methods surpassed current leading approaches based on several clustering validation metrics and reduced computational time significantly. Dhaka Univ. J. Sci. 74(2): 271–282, 2026 (July)