Contrast set mining through subgroup discovery applied to brain Ischaemina data

Petra Kralj; Nada Lavrač; Dragan Gamberger; Antonija Krstǎić

doi:10.1007/978-3-540-71701-0_61

Contrast set mining through subgroup discovery applied to brain Ischaemina data

Petra Kralj^*
, Nada Lavrač
, Dragan Gamberger
, Antonija Krstǎić

^*Corresponding author for this work

Jožef Stefan Institute
University of Nova Gorica
Ruder Boskovic Institute
University of Zagreb

Research output: Contribution to Book/Report types › Conference contribution › peer-review

Abstract (may include machine translation)

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.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
Publisher	Springer Verlag
Pages	579-586
Number of pages	8
ISBN (Print)	9783540717003
DOIs	https://doi.org/10.1007/978-3-540-71701-0_61
State	Published - 2007
Externally published	Yes
Event	11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China Duration: 22 May 2007 → 25 May 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4426 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Country/Territory	China
City	Nanjing
Period	22/05/07 → 25/05/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-540-71701-0_61

Cite this

Kralj, P., Lavrač, N., Gamberger, D., & Krstǎić, A. (2007). Contrast set mining through subgroup discovery applied to brain Ischaemina data. In Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings (pp. 579-586). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4426 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-71701-0_61

Kralj, Petra ; Lavrač, Nada ; Gamberger, Dragan et al. / Contrast set mining through subgroup discovery applied to brain Ischaemina data. Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. Springer Verlag, 2007. pp. 579-586 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

}

Kralj, P, Lavrač, N, Gamberger, D & Krstǎić, A 2007, Contrast set mining through subgroup discovery applied to brain Ischaemina data. in Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4426 LNAI, Springer Verlag, pp. 579-586, 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, Nanjing, China, 22/05/07. https://doi.org/10.1007/978-3-540-71701-0_61

Contrast set mining through subgroup discovery applied to brain Ischaemina data. / Kralj, Petra; Lavrač, Nada; Gamberger, Dragan et al.
Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. Springer Verlag, 2007. p. 579-586 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4426 LNAI).

Research output: Contribution to Book/Report types › Conference contribution › peer-review

TY - GEN

T1 - Contrast set mining through subgroup discovery applied to brain Ischaemina data

AU - Kralj, Petra

AU - Lavrač, Nada

AU - Gamberger, Dragan

AU - Krstǎić, Antonija

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/38049173183

U2 - 10.1007/978-3-540-71701-0_61

DO - 10.1007/978-3-540-71701-0_61

M3 - Conference contribution

AN - SCOPUS:38049173183

SN - 9783540717003

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 579

EP - 586

BT - Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings

PB - Springer Verlag

T2 - 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007

Y2 - 22 May 2007 through 25 May 2007

ER -

Kralj P, Lavrač N, Gamberger D, Krstǎić A. Contrast set mining through subgroup discovery applied to brain Ischaemina data. In Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. Springer Verlag. 2007. p. 579-586. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-71701-0_61

Contrast set mining through subgroup discovery applied to brain Ischaemina data

Abstract (may include machine translation)

Publication series

Conference

UN SDGs

Access to Document

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Cite this