PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Contrast Set Mining through Subgroup Discovery Applied to Brain Ischaemia Data
Petra Kralj, Nada Lavrac, Dragan Gamberger and Antonija Krstačić
In: the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), 22-25 May 2007, Nanjing, China.


Contrast set mining aims at finding differences between different groups. This paper shows that a contrast set mining task can be transformed to a subgroup discovery task whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect to the given property of interest. The proposed approach to contrast set mining through subgroup discovery was successfully applied to the analysis of records of patients with brain stroke (confirmed by a positive CT test), in contrast with patients with other neurological symptoms and disorders (having normal CT test results). Detection of coexisting risk factors, as well as description of characteristic patient subpopulations are important outcomes of the analysis.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3229
Deposited By:Petra Kralj
Deposited On:24 January 2008