@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}
}
@conference {1681710,
	title = {Knowledge Extraction from Fuzzy Data for Estimating Consumer Behavior Models},
	booktitle = {2006 IEEE International Conference on Fuzzy Systems},
	year = {2006},
	month = {July},
	pages = {164-170},
	abstract = {For certain problems of casual modeling in marketing, the information is obtained by means of questionnaires. When these questionnaires include more than one item for each observable variable, the value of this variable can not be assigned a number, but a potentially scattered set of values. In this paper, we propose to represent the information contained in this set of values by means of a fuzzy number. A novel fuzzy statistics-based interpretation of the semantic of a fuzzy set will be used for this purpose, as we will consider that this fuzzy number is a nested family of confidence intervals for a central tendency measure of the value of the variable. A genetic learning algorithm, able to extract association fuzzy rules from this data, is also proposed. The accuracy of the model will be expressed by means of a fuzzy-valued function. We propose to jointly minimize this function and the complexity of the rule based model with multicriteria genetic algorithms, that in turn will depend on a fuzzy ranking-based ordering of individuals.},
	keywords = {Artificial intelligence, association fuzzy rule extraction, casual modeling, Computer science, Computer science education, Consumer behavior, consumer behavior model, consumer behaviour, data mining, fuzzy data, fuzzy logic, fuzzy number, fuzzy ranking, fuzzy set theory, Fuzzy sets, fuzzy statistics, genetic algorithms, knowledge extraction, learning (artificial intelligence), learning algorithm, marketing data processing, multicriteria genetic algorithm, Scattering, semantic interpretation, statistical analysis},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2006.1681710},
	author = {J. Casillas and L. S{\'a}nchez}
}
