  Grouping similar objects is a fundamental tool of scientific analysis,
ubiquitous in disciplines from biology and chemistry to astronomy and pattern
recognition. Inspired by the torque balance that exists in gravitational
interactions when galaxies merge, we propose a novel clustering method based on
two natural properties of the universe: mass and distance. The concept of
torque describing the interactions of mass and distance forms the basis of the
proposed parameter-free clustering algorithm, which harnesses torque balance to
recognize any cluster, regardless of shape, size, or density. The gravitational
interactions govern the merger process, while the concept of torque balance
reveals partitions that do not conform to the natural order for removal.
Experiments on benchmark data sets show the enormous versatility of the
proposed algorithm.
