Name
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. |
Year | Venue | Keywords |
|---|---|---|
2007 | PAKDD | subgroup discovery task,subgroup discovery,contrast set mining,characteristic patient subpopulations,positive ct test,different group,normal ct test result,brain stroke,coexisting risk factor,brain ischaemina data,contrast set mining task,risk factors |
Field | DocType | Volume |
Data mining,Computer science,Contrast set,Stroke,Association rule learning,Subgroup analysis,Artificial intelligence,Machine learning | Conference | 4426 |
ISSN | Citations | PageRank |
0302-9743 | 9 | 0.65 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petra Kralj | 1 | 52 | 2.30 |
| Nada Lavrač | 2 | 989 | 72.19 |
| Dragan Gamberger | 3 | 757 | 60.53 |
| Antonija Krstačić | 4 | 37 | 2.81 |