Subgroup Discovery Algorithms: A Survey and Empirical Evaluation

Abstract

Subgroup discovery is a data mining technique that discovers interesting associations among different variables with respect to a property of interest. Existing subgroup discovery methods employ different strategies for searching, pruning and ranking subgroups. It is very crucial to learn which features of a subgroup discovery algorithm should be considered for generating quality subgroups. In this regard, a number of reviews have been conducted on subgroup discovery. Although they provide a broad overview on some popular subgroup discovery methods, they employ few datasets and measures for subgroup evaluation. In the light of the existing measures, the subgroups cannot be appraised from all perspectives. Our work performs an extensive analysis on some popular subgroup discovery methods by using a wide range of datasets and by defining new measures for subgroup evaluation. The analysis result will help with understanding the major subgroup discovery methods, uncovering the gaps for further improvement and selecting the suitable category of algorithms for specific application domains.

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

“…For single-cell-level analyses, cellular heterogeneity has the potential to be a significant contributor to missing values as only a select portion of the genome is likely expressed, influencing the downstream molecules present . On a larger scale, AI has been used to explain heterogeneity of complex diseases through subgroup identification in unsupervised workflows . However, the success of this approach has been sparsely validated through follow-up studies.…”

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study

“…There is extensive literature in KDD-SD on the type of exploration of these complex patterns ( Fürnkranz et al, 2012 ). Experiments have shown that the exhaustive search based methods perform better than other methods which prune the search before evaluation ( Helal, 2016 ). This is particularly true when the problem size is reasonable (i.e.…”

“…In this second culture the SD process consists in three main phases: candidate subgroup generation, subgroups evaluation and ranking, and subgroups prunning (e.g., top‐ k pruning) ( Helal, 2016 ). The key issues being more related to the algorithmic search for subgroups than their evaluation.…”

“…Recent theoretical and empirical analyses have elucidated different types of methods to select algorithms suitable for specific domains of application ( Helal, 2016 ). Applying such algorithms to SA requires considering the outcome as the variable of interest.…”

Subgroup Preference Neural Network

“…i.e., the relation of incidence of acute kidney injury (AKI) in patients with COVID-19 [ 15 ]. Existing SD utilises different methodologies for searching, pruning, and ranking subgroups [ 16 ]. Leeper, T. introduced conjoint analysis on subgroup preferences in the study of political preferences to give better interpretations and average marginal component effects [ 17 ].…”