Autonomous Clustering by Fast Find of Mass and Distance Peaks

Clustering is an essential analytical tool across a wide range of scientific fields, including biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel clustering algorithm, called Torque Clustering, as a competitive alternative to existing methods, based on the intuitive principle that a cluster should merge with its nearest neighbor with a higher mass, unless both clusters have relatively large masses and the distance between them is also substantial. By identifying peaks in mass and distance, the algorithm effectively detects and removes incorrect mergers. The proposed method is entirely parameter-free, enabling it to autonomously recognize various cluster types, determine the optimal number of clusters, and identify noise. Extensive experiments on synthetic and real-world data sets demonstrate the algorithm's versatility and consistently strong performance compared to other state-of-the-art methods.

I. Introduction and Related Work

Grouping similar objects to derive insights is a fundamental tool in scientific discovery, widely applied across natural and social sciences, including biology, astronomy, psychology, medicine, and chemistry [1]. In data science, this process is known as clustering, a key method for learning from unlabeled data and one of the three broadest categories of machine learning algorithms.