Contrast set mining through subgroup discovery applied to brain ischaemina data

Authors:
Petra Kralj;Nada Lavrač;Dragan Gamberger;Antonija Krstačic
Affiliations:
Jožef Stefan Institute, Ljubljana, Slovenia;Jožef Stefan Institute, Ljubljana, Slovenia and Nova Gorica Polytechnic, Nova Gorica, Slovenia;Rudjer Boškovic Institute, Zagreb, Croatia;University Hospital of Traumatology, Zagreb, Croatia
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

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.