# AtomSD

Subgroup discovery has long compressed deviation and coverage into a single weighted score, so a user-specified minimum support often has no matching most-deviating subgroup. AtomSD recasts the search as selecting among a finite set of homogeneous regions, called atoms: in theory the optimal subgroup at every support threshold is a union of whole atoms plus at most one partial atom. On 19 UCI benchmark datasets it reaches an average rank of 1.25 across five support thresholds and is best in 78 of 95 dataset-threshold combinations, and it improves subgroups returned by four existing methods when used as post-processing.

Subgroup discovery has long compressed deviation and coverage into a single weighted score, so a user-specified minimum support often has no matching most-deviating subgroup. AtomSD recasts the search as selecting among a finite set of homogeneous regions, called atoms: in theory the optimal subgroup at every support threshold is a union of whole atoms plus at most one partial atom. On 19 UCI benchmark datasets it reaches an average rank of 1.25 across five support thresholds and is best in 78 of 95 dataset-threshold combinations, and it improves subgroups returned by four existing methods when used as post-processing.