@article {808,
	title = {A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams},
	journal = {Information Fusion},
	year = {In Press},
	note = {PID2019-107793GB-I00},
	doi = {https://doi.org/10.1016/j.inffus.2022.10.028},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {807,
	title = {Clustering: An R library to facilitate the analysis and comparison of cluster algorithms},
	journal = {Progress in Artificial Intelligence},
	year = {In Press},
	note = {PID2019-107793GB-I00},
	author = {L.A. P{\'e}rez-Martos and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona}
}
@conference {811,
	title = {A case of study with the Clustering R library to measure the quality of cluster algorithms},
	booktitle = {International Conference on Hybrid Artificial Intelligence Systems (HAIS) - Salamanca 5-8 septembre},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {88-97},
	author = {L.A. P{\'e}rez-Martos and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona}
}
@conference {810,
	title = {Gamificaci{\'o}n mediante juegos de bloques en asignaturas del {\'a}mbito de la Inteligencia Artificial en el Grado en Ingenier{\'\i}a Inform{\'a}tica},
	booktitle = {VI Congreso Internacional sobre Innovaci{\'o}n Pedag{\'o}gica y Praxis Educativa (INNOVAGOG{\'I}A 2022) - 25 al 27 de mayo},
	year = {2022},
	address = {Madrid},
	author = {C. J. Carmona and P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico}
}
@conference {809,
	title = {Uso de metodolog{\'\i}as de aprendizaje invertido para resoluci{\'o}n de la parte pr{\'a}ctica en asignaturas del grado de ingenier{\'\i}a inform{\'a}tica},
	booktitle = {XX Congreso Internacional de Investigaci{\'o}n Educativa (14-17 de junio)},
	year = {2022},
	address = {Santiago de Compostela},
	author = {C. J. Carmona and P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico}
}
@article {797,
	title = {A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams},
	journal = {Expert Systems with Applications},
	volume = {183},
	year = {2021},
	note = { PID2019-107793GB-I00, DOC 00235},
	pages = {115419},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {799,
	title = {E2PAMEA: un algoritmo evolutivo para la extracci{\'o}ni eficiente de patrones emergentes difusos en entornos big data},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	author = {A.M. Garc{\'\i}a-Vico and D. Elizondo and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona}
}
@conference {798,
	title = {FEPDS: Una propuesta para la extracci{\'o}n de patrones emergentes difusos en flujos continuos de datos},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	note = {DOC_0235},
	author = {A.M. Garc{\'\i}a-Vico and H. Seker and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {766,
	title = {A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns},
	booktitle = {15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)},
	volume = {1268},
	year = {2020},
	note = {BES-2016-077738},
	pages = {100},
	abstract = {A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining.
The experimental study presented in this paper faces this new proposal regarding an ensemble of one of the most used algorithms within supervised descriptive rule discovery. Results presents a set of patterns with a major interpretability and precision for the new proposal which could be interesting for experts in real-world applications.},
	keywords = {Emerging pattern mining, Fuzzy patterns, Many objective evolutionary algorithm},
	doi = {https://doi.org/10.1007/978-3-030-57802-2_10},
	author = {C. J. Carmona and P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico and M. J. del Jesus}
}
@article {765,
	title = {E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments},
	journal = {Neurocomputing},
	volume = {415},
	year = {2020},
	note = {TIN2015-68454-R; BES-2016-07773},
	month = {11/2020},
	pages = {60-73},
	abstract = {In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process.

The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives.},
	keywords = {Emerging pattern mining Evolutionary fuzzy systems Multi-objective evolutionary algorithm Big data},
	doi = {https://doi.org/10.1016/j.neucom.2020.07.007},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and D. Elizondo and C. J. Carmona}
}
@article {764,
	title = {FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {28},
	year = {2020},
	note = {BES-2016-077738},
	month = {12/2020},
	pages = { 3193-3203},
	abstract = {Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorized as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns (EPs) are interesting in a data stream context as easy, fast, and reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory, and continuous model updates. In this article, an approach for the extraction of EPs in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential.},
	keywords = {Data stream mining, emerging pattern mining (EPM), evolutionary  fuzzy  systems  (EFSs), multiobjective evolutionary algorithms (EAs)},
	doi = {10.1109/TFUZZ.2020.2992849},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and H. Seker and M. J. del Jesus}
}
@article {272,
	title = {A Big Data Approach for the Extraction of Fuzzy Emerging Patterns},
	journal = {Cognitive Computation},
	volume = {11},
	year = {2019},
	note = {TIN2015-68454-R; BES-2016-077738 },
	month = {01/2019},
	pages = {400{\textendash}417},
	doi = {10.1007/s12559-018-9612-7},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {278,
	title = {Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments},
	journal = {Big Data Analytics},
	volume = {4},
	number = {1},
	year = {2019},
	note = {TIN2015-68454-R; BES-2016-077738},
	pages = {1},
	issn = {2058-6345},
	doi = {10.1186/s41044-018-0038-8},
	url = {https://doi.org/10.1186/s41044-018-0038-8},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {763,
	title = {Subgroup Discovery on Multiple Instance Data},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {12},
	year = {2019},
	note = {TIN2017-83445-P; TIN2015-68454-R},
	month = {12/2019},
	pages = {1602-1612},
	abstract = {To date, the subgroup discovery (SD) task has been considered in problems where a target variable is unequivocally described by a set of features, also known as instance. Nowadays, however, with the increasing interest in data storage, new data structures are being provided such as the multiple instance data in which a target variable value is ambiguously defined by a set of instances. Most of the proposals related to multiple instance data are based on predictive tasks and no supervised descriptive analysis can be provided when data is organized in this way. At this point, the aim of this work is to extend the SD task to cope with this type of data. SD is a really interesting task that aims at discovering interesting relationships between different features with respect to a specific target variable that is of interest for the user or the problem under study. In this regard, this paper presents three different approaches for mining interesting subgroups in multiple instance problems. The proposed models represent three different ways of tackling the problem and they are based on three well-known algorithms in the SD field: SD-Map (exhaustive search approach), CGBA-SD (Comprehensible Grammar-Based Algorithm for Subgroup Discovery) and NMEEF-SD (multi-objective evolutionary fuzzy system). The proposals have been tested on a wide set of datasets, including 10 real-world and 20 synthetic datasets, aiming at describing how the three methodologies behave on different scenarios. Any comparison is unfair since they are completely different methodologies.},
	keywords = {Metaheuristics, Multi-instance data, Subgroup discovery, Supervised descriptive patterns},
	doi = {https://doi.org/10.2991/ijcis.d.191213.001},
	author = {J. M. Luna and C. J. Carmona and A.M. Garc{\'\i}a-Vico and M. J. del Jesus and S. Ventura}
}
@article {261,
	title = {An Overview of Emerging Pattern Mining in Supervised Descriptive Rule Discovery: Taxonomy, Empirical Study, Trends and Prospects},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {8},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	doi = {10.1002/widm.1231},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and D. Mart{\'\i}n and M. Garc{\'\i}a-Borroto and M. J. del Jesus}
}
@article {628,
	title = {Improvement of subgroup descriptions in noisy data by detecting exceptions},
	journal = {Progress in Artificial Intelligence},
	volume = {7},
	year = {2018},
	note = {TIN2015-68454-R},
	pages = {55-64},
	doi = {10.1007/s13748-017-0131-7},
	author = {P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico and C. J. Carmona and M. J. del Jesus}
}
@conference {275,
	title = {Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de grandes vol{\'u}menes de datos y flujos continuos de datos},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {1402-1407},
	author = {A.M. Garc{\'\i}a-Vico}
}
@article {271,
	title = {MOEA-EFEP: Multi-Objective Evolutionary Algorithm for Extracting Fuzzy Emerging Patterns},
	journal = {IEEE Transaction on Fuzzy Systems},
	volume = {26},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {2861-2872},
	doi = {10.1109/TFUZZ.2018.2814577},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {273,
	title = {MOEA-EFEP: Un algoritmo evolutivo multi-objetivo para la extracción de patrones emergentes difusos},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {671-672},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {274,
	title = {Una primera aproximaci{\'o}n para la extracci{\'o}n de patrones emergentes en flujos continuos de datos},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {1093-1098},
	address = {Mejor trabajo del II Workshop en Big Data y An{\'a}lisis de Datos Escalable - BigDADE 2018},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {263,
	title = {A First Approach to Handle Emergining Patterns Mining on Big Data Problems: The EvAEFP-Spark Algorithm},
	booktitle = {Proc. of the 2017 IEEE International Conference on Fuzzy Systems},
	year = {2017},
	pages = {1-6},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and M. J. del Jesus and C. J. Carmona}
}
@conference {265,
	title = {An{\'a}lisis de Diferentes Tipos de Reglas en Sistemas Difusos Evolutivos para Miner{\'\i}a de Patrones Emergentes},
	booktitle = {Proc. of the XII Spanish Conference on Metaheuristics, Evolutive and Bioinspired Algorithms (MAEB 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	pages = {876{\textendash}885},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and M. J. del Jesus}
}
@conference {264,
	title = {Impact of the Type of Rule in Fuzzy Emerging Pattern Mining on a Big Data Approach},
	booktitle = {Proc. of the II International symposium on Fuzzy and Rough Sets (ISFUROS 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@conference {603,
	title = {Analysing Concentrating Photovoltaics Technology through the use of Emerging Pattern Mining},
	booktitle = {Proceedings of the 11th International Conference on Soft Computing Models in Industrial and Environmental Applications},
	year = {2016},
	note = {ENE2009-08302, P09-TEP-5045, TIN2015-68454-R},
	author = {A.M. Garc{\'\i}a-Vico and J. Montes and J. Aguilera and C. J. Carmona and M. J. del Jesus}
}
@conference {604,
	title = {Miner{\'\i}a de Patrones Emergentes: Una oportunidad para la extracci{\'o}n evolutiva de conocimiento},
	booktitle = {XI Congreso Espa{\~n}ol de Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)},
	year = {2016},
	note = {TIN2015-68454-R},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {605,
	title = {Subgroup Discovery with Evolutionary Fuzzy Systems in R: the SDEFSR Package},
	journal = {The R Journal},
	volume = {8},
	year = {2016},
	note = {TIN2015-68854-R},
	pages = {307-323},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {262,
	title = {The Influence of Noise on the Evolutionary Fuzzy Systems for Subgroup Discovery},
	journal = {Soft Computing},
	volume = {20},
	year = {2016},
	note = {TIN2015-68454-R, TIN2014-57251-P, P11-TIC-7765, P12-TIC-2958},
	pages = {4313-4330},
	doi = {10.1007/s00500-016-2300-1},
	author = {J. Luengo and A.M. Garc{\'\i}a-Vico and M.D. P{\'e}rez-Godoy and C. J. Carmona}
}
@conference {Cpggj15,
	title = {An{\'a}lisis descriptivo mediante aprendizaje supervisado basado en patrones emergentes},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos},
	year = {2015},
	note = {TIN2012-33856},
	pages = {685-694},
	author = {C. J. Carmona and F. Pulgar-Rubio and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {Gcgcj15,
	title = {Usando Algoritmos de Descubrimiento de Subgrupos en R: El Paquete SDR},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos},
	year = {2015},
	note = {TIN2012-33856},
	pages = {739-748},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
