2018
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Salado-Cid, R; Ramírez, A; Romero, J R On the need of opening the Big Data landscape to everyone: challenges and new trends Incollection Linnhoff-Popien, C; Schneider, R; Zaddach, M (Ed.): Digital Marketplaces Unleashed, pp. 675-687, Springer Berlin Heidelberg, Berlin, Heidelberg, 2018, ISBN: 978-3-662-49275-8. Links | BibTeX | Tags: Big Data Mining, Data Science, Scientific workflows @incollection{Salado-Cid-2018-DMU,
title = {On the need of opening the Big Data landscape to everyone: challenges and new trends},
author = {Salado-Cid, R. and Ram\^{i}rez, A. and Romero, J. R.},
editor = {Linnhoff-Popien, C. and Schneider, R. and Zaddach, M.},
url = {https://doi.org/10.1007/978-3-662-49275-8_60},
doi = {10.1007/978-3-662-49275-8_60},
isbn = {978-3-662-49275-8},
year = {2018},
date = {2018-01-01},
booktitle = {Digital Marketplaces Unleashed},
pages = {675-687},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
keywords = {Big Data Mining, Data Science, Scientific workflows},
pubstate = {published},
tppubtype = {incollection}
}
|
Cano, A A survey on graphic processing unit computing for large-scale data mining Journal Article Wiley Interdisciplinary Reviews - Data Mining and Knowledge Discovery, 8 (1), pp. e1232, 2018. Links | BibTeX | Tags: Big Data Mining, Classification, GP-GPU, Scalability @article{2017-WIRES-GPUDM,
title = {A survey on graphic processing unit computing for large-scale data mining},
author = {Cano, A.},
url = {http://dx.doi.org/10.1002/widm.1232},
doi = {10.1002/widm.1232},
year = {2018},
date = {2018-01-01},
journal = {Wiley Interdisciplinary Reviews - Data Mining and Knowledge Discovery},
volume = {8},
number = {1},
pages = {e1232},
keywords = {Big Data Mining, Classification, GP-GPU, Scalability},
pubstate = {published},
tppubtype = {article}
}
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Luna, J M; Padillo, F; M., Pechenizkiy; Ventura, S Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data Journal Article IEEE Trans. Cybernetics, 48 (10), pp. 2851–2865, 2018. Links | BibTeX | Tags: Association Rule Mining, Big Data Mining, Hadoop, Pattern Mining, Scalability, Spark @article{LunaPPV18,
title = {Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data},
author = {Luna, J. M. and Padillo, F. and Pechenizkiy M. and Ventura, S.},
url = {https://doi.org/10.1109/TCYB.2017.2751081},
doi = {10.1109/TCYB.2017.2751081},
year = {2018},
date = {2018-01-01},
journal = {IEEE Trans. Cybernetics},
volume = {48},
number = {10},
pages = {2851--2865},
keywords = {Association Rule Mining, Big Data Mining, Hadoop, Pattern Mining, Scalability, Spark},
pubstate = {published},
tppubtype = {article}
}
|
2017
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Padillo, F; Luna, J M; Ventura, S An evolutionary algorithm for mining rare association rules: A Big Data approach Inproceedings 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, pp. 2007–2014, 2017. Links | BibTeX | Tags: Association Rule Mining, Big Data Mining, Evolutionary Algorithms, Grammar-Based Genetic Programming, Hadoop, Pattern Mining, Spark, Unsupervised Learning @inproceedings{PadilloLV17,
title = {An evolutionary algorithm for mining rare association rules: A Big Data approach},
author = {Padillo, F. and Luna, J. M. and Ventura, S.},
url = {https://doi.org/10.1109/CEC.2017.7969547},
doi = {10.1109/CEC.2017.7969547},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebasti\'{a}n, Spain, June 5-8, 2017},
pages = {2007--2014},
keywords = {Association Rule Mining, Big Data Mining, Evolutionary Algorithms, Grammar-Based Genetic Programming, Hadoop, Pattern Mining, Spark, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Padillo, F; Luna, J M; Ventura, S Exhaustive search algorithms to mine subgroups on Big Data using Apache Spark Journal Article Progress in Artificial Intelligence, 6 (2), pp. 145–158, 2017. Links | BibTeX | Tags: Big Data Mining, Hadoop, Pattern Mining, Spark, Subgroup Discovery @article{PadilloLV17b,
title = {Exhaustive search algorithms to mine subgroups on Big Data using Apache Spark},
author = {Padillo, F. and Luna, J. M. and Ventura, S.},
doi = {10.1007/s13748-017-0112-x},
year = {2017},
date = {2017-01-01},
journal = {Progress in Artificial Intelligence},
volume = {6},
number = {2},
pages = {145--158},
keywords = {Big Data Mining, Hadoop, Pattern Mining, Spark, Subgroup Discovery},
pubstate = {published},
tppubtype = {article}
}
|
Luna, J M; Padillo, F; Pechenizkiy, M; Ventura, S Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data Journal Article IEEE Transactions on Cybernetics, 99 (PP), pp. 1-15, 2017. Links | BibTeX | Tags: Association Rule Mining, Big Data Mining, Hadoop, Pattern Mining, Spark, Unsupervised Learning @article{LunaIEEE17b,
title = {Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data},
author = {Luna, J. M. and Padillo, F. and Pechenizkiy, M. and Ventura, S.},
doi = {10.1109/TCYB.2017.2751081},
year = {2017},
date = {2017-01-01},
journal = {IEEE Transactions on Cybernetics},
volume = {99},
number = {PP},
pages = {1-15},
keywords = {Association Rule Mining, Big Data Mining, Hadoop, Pattern Mining, Spark, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
|
2016
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Padillo, F; Luna, J M; Ventura, S Subgroup discovery on Big Data: exhaustive methodologies using Map-Reduce Inproceedings Proceedings of the 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 1684–1691, Tianjin, China, 2016. Links | BibTeX | Tags: Big Data Mining, Pattern Mining, Scalability, Spark, Subgroup Discovery @inproceedings{Padillo:BigDataSE16:16,
title = {Subgroup discovery on Big Data: exhaustive methodologies using Map-Reduce},
author = {Padillo, F. and Luna, J. M. and Ventura, S.},
url = {http://dx.doi.org/10.1109/TrustCom/BigDataSE/ISPA.2016.256},
doi = {10.1109/TrustCom/BigDataSE/ISPA.2016.256},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the 2016 IEEE Trustcom/BigDataSE/ISPA},
pages = {1684--1691},
address = {Tianjin, China},
keywords = {Big Data Mining, Pattern Mining, Scalability, Spark, Subgroup Discovery},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Luna, J M; Cano, A; Pechenizkiy, M; Ventura, S Speeding-Up Association Rule Mining With Inverted Index Compression Journal Article IEEE Transactions on Cybernetics, 46 (12), pp. 3059-3072, 2016. Links | BibTeX | Tags: Association Rule Mining, Big Data Mining, Pattern Mining, Unsupervised Learning @article{LunaSpeedingUp,
title = {Speeding-Up Association Rule Mining With Inverted Index Compression},
author = {Luna, J. M. and Cano, A. and Pechenizkiy, M. and Ventura, S.},
url = {http://dx.doi.org/ 10.1109/TCYB.2015.2496175},
doi = {10.1109/TCYB.2015.2496175},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions on Cybernetics},
volume = {46},
number = {12},
pages = {3059-3072},
keywords = {Association Rule Mining, Big Data Mining, Pattern Mining, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
|
Padillo, F; Luna, J M; Cano, A; Ventura, S A Data Structure to Speed-Up Machine Learning Algorithms on Massive Datasets Inproceedings Proceedings of the 11th International Conference on Hybrid Artificial Intelligence Systems, pp. 365-376, Seville, Spain, 2016. Links | BibTeX | Tags: Big Data Mining, Scalability @inproceedings{Padillo:HAIS16:2016,
title = {A Data Structure to Speed-Up Machine Learning Algorithms on Massive Datasets},
author = {Padillo, F. and Luna, J. M. and Cano, A. and Ventura, S.},
url = {http://dx.doi.org/10.1007/978-3-319-32034-2_31},
doi = {10.1007/978-3-319-32034-2_31},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the 11th International Conference on Hybrid Artificial Intelligence Systems},
pages = {365-376},
address = {Seville, Spain},
series = {HAIS 2016},
keywords = {Big Data Mining, Scalability},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Padillo, F; Luna, J M; Ventura, S; Herrera, F Algoritmo de programación genética gramatical para la extracción de reglas de asociación en Big Data usando el paradigma MapReduce Inproceedings XI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'16), pp. 137-148, 2016, ISBN: 978-84-9012-632-5. BibTeX | Tags: Association Rule Mining, Big Data Mining, Evolutionary Algorithms, Grammar-Based Genetic Programming, Hadoop, Pattern Mining, Scalability, Spark, Unsupervised Learning @inproceedings{Padillo:MAEB16:16,
title = {Algoritmo de programaci\'{o}n gen'{e}tica gramatical para la extracci\'{o}n de reglas de asociaci\'{o}n en Big Data usando el paradigma MapReduce},
author = {Padillo, F. and Luna, J. M. and Ventura, S. and Herrera, F.},
isbn = {978-84-9012-632-5},
year = {2016},
date = {2016-01-01},
booktitle = {XI Congreso Espa\~{n}ol sobre Metaheur\^{i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB'16)},
pages = {137-148},
keywords = {Association Rule Mining, Big Data Mining, Evolutionary Algorithms, Grammar-Based Genetic Programming, Hadoop, Pattern Mining, Scalability, Spark, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Padillo, F; Luna, J M; Ventura, S Búsquedas exhaustivas de subgrupos con MapReduce en Big Data Inproceedings VIII Simposio Teoría y Aplicaciones de Minería de Datos (TAMIDA'16), pp. 779-788, 2016, ISBN: 978-84-9012-632-5. BibTeX | Tags: Big Data Mining, Pattern Mining, Scalability, Spark @inproceedings{Padillo:TAMIDA16_SD:16,
title = {B\'{u}squedas exhaustivas de subgrupos con MapReduce en Big Data},
author = {Padillo, F. and Luna, J. M. and Ventura, S.},
isbn = {978-84-9012-632-5},
year = {2016},
date = {2016-01-01},
booktitle = {VIII Simposio Teor\^{i}a y Aplicaciones de Miner\^{i}a de Datos (TAMIDA'16)},
pages = {779-788},
keywords = {Big Data Mining, Pattern Mining, Scalability, Spark},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Padillo, F; Luna, J M; Ventura, S Minería de patrones en Big Data Inproceedings VIII Simposio Teoría y Aplicaciones de Minería de Datos (TAMIDA'16), pp. 769-778, 2016, ISBN: 978-84-9012-632-5. BibTeX | Tags: Association Rule Mining, Big Data Mining, Hadoop, Pattern Mining, Scalability, Unsupervised Learning @inproceedings{Padillo:TAMIDA16:16,
title = {Miner\^{i}a de patrones en Big Data},
author = {Padillo, F. and Luna, J. M. and Ventura, S.},
isbn = {978-84-9012-632-5},
year = {2016},
date = {2016-01-01},
booktitle = {VIII Simposio Teor\^{i}a y Aplicaciones de Miner\^{i}a de Datos (TAMIDA'16)},
pages = {769-778},
keywords = {Association Rule Mining, Big Data Mining, Hadoop, Pattern Mining, Scalability, Unsupervised Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Ventura, S; Luna, J M; Cano, A Big Data on Real-World Applications Book 1st, InTech, 2016, ISBN: 978-953-51-2490-0. Links | BibTeX | Tags: Big Data Mining @book{VenturaLC16,
title = {Big Data on Real-World Applications},
author = {Ventura, S. and Luna, J. M. and Cano, A.},
url = {http://www.intechopen.com/books/big-data-on-real-world-applications},
doi = {10.5772/61396},
isbn = {978-953-51-2490-0},
year = {2016},
date = {2016-01-01},
publisher = {InTech},
edition = {1st},
keywords = {Big Data Mining},
pubstate = {published},
tppubtype = {book}
}
|