Autonomous Clustering by Fast Find of Mass and Distance Peaks

Jie Yang, Chin-teng Lin

Abstract:

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 largemasses 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.