@inproceedings{a4e766968e4d422ebe22122566256aca,
title = "Contrast set mining through subgroup discovery applied to brain Ischaemina data",
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.",
author = "Petra Kralj and Nada Lavra{\v c} and Dragan Gamberger and Antonija Krstǎi{\'c}",
year = "2007",
doi = "10.1007/978-3-540-71701-0_61",
language = "English",
isbn = "9783540717003",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "579--586",
booktitle = "Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings",
note = "11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 ; Conference date: 22-05-2007 Through 25-05-2007",
}