@article {795,
	title = {Temporal association rule mining: An overview considering the time variable as an integral or implied component},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {10},
	year = {2020},
	note = {TIN2017-89517-P; P18-RT-2248},
	month = {04/2020},
	abstract = {Association rules are commonly used to provide decision-makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real-life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well-defined framework that allows researchers both to easily locate the previous proposals and to propose well-grounded methods in the future. To accomplish both objectives, a two-level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed.

This article is categorized under:

    Technologies > Association Rules
    Algorithmic Development > Association Rules
},
	keywords = {calendar association rules, cyclic association rules, data mining, sequential association rules, temporal association rules},
	doi = {https://www.doi.org/10.1002/widm.1367},
	author = {Alberto Segura-Delgado and M. J. Gacto and Alcal{\'a}, Rafael and J. Alcal{\'a}-Fdez}
}
@article {794,
	title = {Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	month = {08/2019},
	pages = { 108916-108939},
	abstract = {In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, Regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component Regression, Multivariate Adaptive Regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.},
	keywords = {data mining, experimental study., regression algorithms, supervised learning},
	doi = {https://doi.org/10.1109/ACCESS.2019.2933261},
	author = {M. J. Gacto and Jose Manuel Soto-Hidalgo and J. Alcal{\'a}-Fdez and Alcal{\'a}, Rafael}
}
@article {270,
	title = {Evolutionary Data Mining and Applications: A Revision on the Most Cited Papers from the Last 10 Years (2007-2017)},
	journal = {WIREs Data Mining and Knowledge Discover},
	volume = {8},
	year = {2018},
	note = {TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765},
	pages = {1-17},
	doi = {https://doi.org/10.1002/widm.1239},
	author = {R. Alcal{\'a} and M. J. Gacto and J. Alcal{\'a}-Fdez}
}
@article {753,
	title = {KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining.},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {10},
	number = {1},
	year = {2017},
	pages = {1238-1249},
	issn = {1875-6891},
	author = {I. Triguero and S. Gonzalez and J.M. Moyano and S. Garc{\'\i}a and J. Alcal{\'a}-Fdez and J. Luengo and A. Fern{\'a}ndez and M. J. del Jesus and L. S{\'a}nchez and F. Herrera and ATLANTIS PRESS.}
}
@article {simidat242,
	title = {Mining Fuzzy Association Rules from Low Quality Data},
	journal = {Soft Computing},
	volume = {16},
	number = {5},
	year = {2012},
	pages = {883-901},
	doi = {10.1007/s00500-011-0775-3},
	author = {A. Palacios and M. J. Gacto and J. Alcal{\'a}-Fdez}
}
@article {simidat237,
	title = {KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework},
	journal = {Journal of Multiple-Valued Logic and Soft Computing},
	volume = {17},
	number = {2-3},
	year = {2011},
	pages = {255-287},
	author = {J. Alcal{\'a}-Fdez and A. Fern{\'a}ndez and J. Luengo and J. Derrac and S. Garc{\'\i}a and L. S{\'a}nchez and F. Herrera}
}
@article {simidat100,
	title = {Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems},
	journal = {Applied Intelligence},
	volume = {31},
	number = {1},
	year = {2009},
	pages = {15-30},
	doi = {10.1007/s10489-007-0107-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat95,
	title = {KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems},
	journal = {Soft Computing},
	volume = {13},
	number = {3},
	year = {2009},
	pages = {307-318},
	doi = {10.1007/s00500-008-0323-y},
	author = {J. Alcal{\'a}-Fdez and L. S{\'a}nchez and S. Garc{\'\i}a and M. J. del Jesus and S. Ventura and J.M. Garrell and J. Otero and C. Romero and J. Bacardit and V. M. Rivas and J.C. Fern{\'a}ndez and F. Herrera}
}
@article {simidat99,
	title = {Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms},
	journal = {Fuzzy Sets and Systems},
	volume = {160},
	number = {7},
	year = {2009},
	pages = {905-921},
	doi = {10.1016/j.fss.2008.05.012},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@conference {simidat44,
	title = {KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems},
	booktitle = {3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)},
	year = {2008},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {83-88},
	address = {WittenBommerholz (Germany)},
	author = {J. Alcal{\'a}-Fdez and S. Garc{\'\i}a and F. J. Berlanga and A. Fern{\'a}ndez and L. S{\'a}nchez and M. J. del Jesus and F. Herrera}
}
@inbook {simidat110,
	title = {On the use of Multiobjective Genetic Algorithms to Improve the Accuracy-Interpretability Trade-Off of Fuzzy Rule-Based Systems},
	booktitle = {Multi-objective Evolutionary Algorithms for Knowledge Discovery from Data Bases},
	volume = {98},
	year = {2008},
	isbn = {978-3-540-77466-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {A. Ghosh and S. Dehuri and S. Ghosh}
}
@conference {simidat108,
	title = {A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1367-1372},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto}
}
@article {simidat97,
	title = {A Multi-objectiveGenetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {15},
	number = {5},
	year = {2007},
	pages = {539{\textendash}557},
	doi = {10.1142/S0218488507004868},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera and J. Alcal{\'a}-Fdez}
}
@conference {simidat109,
	title = {Aprendizaje Evolutivo de los Contextos de las Funciones de Pertenencia para Extraer Reglas de Asociaci{\'o}n Difusas},
	booktitle = {Proceedings of the II Congreso Espa{\~n}ol de Inform{\'a}tica (CEDI 2007). II Simposio sobre L{\'o}gica Fuzzy y Soft Computing (LFSC)},
	year = {2007},
	month = {September},
	pages = {25-32},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@conference {simidat107,
	title = {Genetic Learning of Membership Functions for Mining Fuzzy Association Rules},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1538-1543},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat96,
	title = {Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation},
	journal = {Soft Computing},
	volume = {11},
	number = {5},
	year = {2007},
	pages = {401-419},
	doi = {10.1007/s00500-006-0106-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@inbook {simidat104,
	title = {Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System},
	booktitle = {Fuzzy Applications in Industrial Engineering, Studies in Fuzziness and Soft Computing},
	volume = {201},
	year = {2006},
	pages = {89-117},
	isbn = {978-3-540-33516-0},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {Cengiz Kahraman}
}
@conference {simidat38,
	title = {Genetic Lateral and Amplitude Tuning with Rule Selection for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems},
	booktitle = {19th International Conference on Industrial \& Engineering Applications of Artificial Intelligence \& Expert Systems (IEA/AIE)},
	series = {LNCS},
	volume = {4031},
	year = {2006},
	pages = {452-461},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Annecy (France)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat39,
	title = {Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection},
	booktitle = {Modeling Decisions for Artificial Intelligence (MDAI)},
	series = {LNCS},
	volume = {3885},
	year = {2006},
	pages = {317-328},
	publisher = {Springer},
	organization = {Springer},
	address = {Tarragona (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat105,
	title = {Obtaining Compact and Still Accurate Linguistic Fuzzy Rule-Based Systems by Using Multi-Objetive Genetic Algorithms},
	booktitle = {Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS)},
	year = {2006},
	pages = {53-62},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat106,
	title = {Obtenci{\'o}n de Sistemas Basados en Reglas Difusas Precisos y Compactos Mediante Algoritmos Gen{\'e}ticos Multiobjetivo},
	booktitle = {Proceedings of the XIII Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2006},
	pages = {1-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto}
}
@conference {simidat102,
	title = {Ajuste Evolutivo Lateral y de Amplitud de etiquetas para Sistemas Basados en Reglas Difusas},
	booktitle = {Simposio de Inteligencia Computacional (SICO)},
	year = {2005},
	pages = {481-488},
	address = {Granada (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat103,
	title = {Genetic Lateral and Amplitude Tuning of Membership Functions for Fuzzy Systems},
	booktitle = {Proceedings of the 2nd International Conference on Machine Intelligence (ACIDCA-ICMI)},
	year = {2005},
	pages = {589-595},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
