@conference {8015572,
	title = {Mining association rules in R using the package RKEEL},
	booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2017},
	month = {July},
	pages = {1-6},
	abstract = {The discovery of fuzzy associations comprises a collection of data mining methods used to extract knowledge from large data sets. Although there is an extensive catalog of specialized algorithms that cover different aspects of the problem, the most recent approaches are not yet packaged in mainstream software environments. This makes it difficult to incorporate novel association rules methods to the data mining workflow. In this paper an extension of the RKEEL package is described that allows calling from the programming language R to those association rules methods contained in KEEL, which is one of the most comprehensive open source software suites. The potential of the proposed tool is illustrated through a case study comprising seven real-world datasets.},
	keywords = {Computer science, data mining, data mining methods, data mining workflow, Electronic mail, fuzzy associations, fuzzy set theory, knowledge extraction, large data sets, Measurement, mining association rules, Open source software, open source software suites, programming language R, programming languages, public domain software, real-world datasets, RKEEL package, Software algorithms, software environments, software packages, Tools},
	issn = {1558-4739},
	doi = {10.1109/FUZZ-IEEE.2017.8015572},
	author = {O. S{\'a}nchez and J. M. Moyano and L. S{\'a}nchez and J. Alc{\'a}la-F{\'a}dez}
}
