Papers › Subgroups: A Python library for Subgroup Discovery
Subgroups: A Python library for Subgroup Discovery
Antonio Lopez-Martinez-Carrasco, Jose M. Juarez, Manuel Campos, Francisco Mora-Caselles
This manuscript introduces Subgroups, an openly accessible Python library designed to ease the use of Subgroup Discovery (SD) algorithms for machine learning and data science. The Subgroups Library offers several advantages: (1) Efficiency Enhancement: Developed in native Python, unlike other software available, the library prioritizes efficiency to ensure seamless execution of SD algorithms; (2) User-Friendly Interface: Modeled after the popular scikit-learn framework, the library boasts an intuitive interface, streamlining the utilization process for practitioners and non-expert programmers; (3) Trustworthy Algorithm Implementations: Drawing from scientific publications authored by leading experts, the Subgroups Library incorporates rigorously tested algorithmic implementations, ensuring reliability and accuracy in results; (4) Customization and Expansion: The modular architecture of the library facilitates effortless integration of additional quality measures, data structures, and SD algorithms, empowering users to tailor their analyses to specific needs and explore new avenues of research. Furthermore, the Subgroups Library has been successfully employed in diverse scientific papers and projects, underscoring its efficacy and versatility as a valuable tool for SD exploration and application.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections