CSM-SD: Methodology for contrast set mining through subgroup discovery

• Published in: Journal of biomedical informatics Vol. 42; no. 1; pp. 113 - 122
• Main Authors: Kralj Novak, Petra, Lavrač, Nada, Gamberger, Dragan, Krstačić, Antonija
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2009
• Subjects: Algorithms; Artificial Intelligence; Brain ischemia; Brain Ischemia - diagnosis; Chi-Square Distribution; Contrast set mining; Decision Trees; Descriptive rules; Humans; Information Storage and Retrieval - methods; Intracranial Embolism - diagnosis; Intracranial Thrombosis - diagnosis; Medical Records Systems, Computerized; Pattern Recognition, Automated - methods; Prognosis; Risk Factors; Statistics, Nonparametric; Subgroup discovery; Supporting factors; Brain ischemia; Supporting factors; Contrast set mining; Subgroup discovery; Descriptive rules
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2008.08.007

This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups. As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task. The transformation is studied in two learning settings, a one-versus-all and a pairwise contrast set mining setting, uncovering the conditions for each of the two choices. Moreover, the paper shows that the explanatory potential of discovered contrast sets can be improved by offering additional contrast set descriptors, called the supporting factors. The proposed methodology has been applied to uncover distinguishing characteristics of two groups of brain stroke patients, both with rapidly developing loss of brain function due to ischemia:those with ischemia caused by thrombosis and by embolism, respectively.