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

Clustering via torque balance with mass and distance