2002/12/01 by D. Gamberger, Dragan Gamberger, Nada Lavrač +1
Computer Science · Mathematics · #Artificial intelligence #Computer science #Data Mining Algorithms and Applications #Data mining #Heuristic #Imbalanced Data Classification Techniques #Machine Learning and Data Classification #Mathematics #Process (computing) #Programming language #Statistics #Subgroup analysis #Visualization #cs.AI
paper · pdf · doi:10.1613/jair.1089
published as Journal Of Artificial Intelligence Research, Volume 17, pages 501-527, 2002
openalex publication_date 2002/12/01 · arxiv created 2011/06/22 · arxiv updated 2011/06/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.