Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
By Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb. 2009
430 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Alberto Fernández
- M. J. Jesús
- Oscar Cordón
- Himabindu Lakkaraju
- Pedro Pablo Gonzalez
- Cristóbal José Carmona
- Jure Leskovec
In Computational Theory and Mathematics
- Oscar Cordón
- Francisco Herrera
- Alberto Fernández
- Pedro Pablo Gonzalez
- M. J. Jesús
- Jure Leskovec
- Hugo Jair Escalante
- Isabelle M Guyon
In Information Systems
- Himabindu Lakkaraju
- Jure Leskovec
- Alberto Fernández
- Francisco Herrera
- M. J. Jesús
- Pedro Pablo Gonzalez
- Francesco Marcelloni
- Hugo Jair Escalante
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, neutral): “In [14], a survey relating contrast set mining, subgroup mining and emerging pattern mining is presented.”
- Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? (2019, background): “Supervised descriptive rule discovery [50], and in particular subgroup discovery [51], [52], is also another interesting area of study.”
- An overview on subgroup discovery: foundations and applications (2011, background): “" Supervised Descriptive Rule Induction " [78] is a new recently proposed paradigm which includes techniques combining the features of both types of induction, and its main objective is to extract descriptive knowledge from the data of a property of interest.”
- Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, background): “Bussone A, Stumpf S, O’Sullivan D (2015) The role of explanations on trust and reliance in clinical decision support systems. In: Healthcare Informatics (ICHI), 2015 International Conference on, IEEE, pp 160–169 Carton S, Helsby J, Joseph K, Mahmud A, Park Y, Walsh J, Cody C, Patterson CE, Haynes L,”