Improving Fast Density Peak Clustering Using Mass Distance: m-FDPC

Abstract

In this work, we introduce m-FDPC, a mass-based variant of the Fast Density Peak Clustering (FDPC) algorithm, aimed at improving both performance and ease of use in unsupervised learning tasks. Traditional FDPC relies on Euclidean distance and requires careful parameter tuning and data normalization, which can significantly affect clustering outcomes—especially for heterogeneous or high-dimensional datasets. To address these challenges, m-FDPC replaces the conventional Euclidean metric with a mass-based distance measure derived from isolation forests, a method originally designed for anomaly detection. This substitution allows the algorithm to capture local data density and structure more naturally, while eliminating the need for normalization and simplifying the choice of key parameters such as cutoff distance and density thresholds. Comprehensive experiments on synthetic and real-world datasets demonstrate that m-FDPC not only matches or surpasses the performance of well-established clustering techniques such as DBSCAN, K-means, and Euclidean FDPC, but also offers greater robustness, scalability, and interpretability, particularly in high-dimensional or unevenly distributed data scenarios. Results evaluated through metrics like Matching Score and Silhouette Score confirm the algorithm’s superior ability to detect meaningful cluster structures with minimal user intervention. Overall, m-FDPC provides a more efficient, adaptive, and user-friendly framework for density-based clustering, making it a promising tool for diverse applications in data mining, anomaly detection, and exploratory data analysis.