R (>= 3.0), igraph (>= 1.0.0), doParallel, foreach, iterators,
parallel

Subgroup Discovery with Evolutionary Fuzzy Systems in R
c(person("Angel M.", "Garcia", email = "amgv0009@red.ujaen.es", role = c("aut", "cre")),
person("Pedro", "Gonzalez", email = "pglez@ujaen.es", role = c("aut", "cph")),
person("Cristobal J.", "Carmona", email = "ccarmona@ujaen.es", role = c("aut", "cph")),
person("Francisco", "Charte", email = "francisco@fcharte.com", role = "ctb"),
person("Maria J.", "del Jesus", email = "mjjesus@ujaen.es", role = c("aut", "cph"))
)
Implementation of evolutionary fuzzy systems for the data mining task called
"subgroup discovery". It also provide a Shiny App
for make the analysis easier. The algorithms works with data sets provided in
KEEL, ARFF and CSV format and also with data.frame objects.
grDevices, methods, parallel, stats, utils
Angel M. Garcia [aut, cre],
Pedro Gonzalez [aut, cph],
Cristobal J. Carmona [aut, cph],
Francisco Charte [ctb],
Maria J. del Jesus [aut, cph]

Subgroup Discovery with Evolutionary Fuzzy Systems
c(person("Angel M.", "Garcia", email = "agvico@ujaen.es", role = c("aut", "cre")),
person("Pedro", "Gonzalez", email = "pglez@ujaen.es", role = c("aut", "cph")),
person("Cristobal J.", "Carmona", email = "ccarmona@ujaen.es", role = c("aut", "cph")),
person("Francisco", "Charte", email = "francisco@fcharte.com", role = "ctb"),
person("Maria J.", "del Jesus", email = "mjjesus@ujaen.es", role = c("aut", "cph"))
)
Implementation of evolutionary fuzzy systems for the data mining task called
"subgroup discovery". In particular, the algorithms presented in this package are:
M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662>
M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416>
C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200>
C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030>
It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in
KEEL, ARFF and CSV format and also with data.frame objects.