Contrast Set Mining for Distinguishing Between Similar Diseases

By Petra Kralj Novak, Nada Lavrac, Dragan Gamberger, Antonija Krstacic. 2007

38 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jure Leskovec
2. Himabindu Lakkaraju
3. Stephen H. Bach
4. Marinka Zitnik

In Information Systems

1. Himabindu Lakkaraju
2. Jure Leskovec
3. Stephen H. Bach
4. Tong Wang
5. Alexey Ignatiev
6. Joao Marques-Silva
7. Nina Narodytska
8. Finale Doshi-Velez

In Molecular Biology

1. Jure Leskovec
2. Stephen H. Bach
3. William L. Hamilton
4. Stephan Günnemann
5. Marinka Zitnik
6. Petar Velickovic
7. Aleksandar Bojchevski
8. Rex Ying

Citations in the corpus

• Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Similarly, algorithms for problems such as subgroup discovery [24, 33, 40, 46], contrast set learning [3, 4, 30], and emerging pattern mining [17, 19] identify sets of rules to describe the relationships among variables and discover interesting patterns in the data.”
• Rules for contrast sets (2010, unknown): “In [11] the problem of distinguishing between thrombolic brain stroke and embolic brain stroke is addressed using a particular approach to contrast set mining through subgroup discovery.”