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Fuzzy Models for Big Data Mining

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Fuzzy Logic and Applications (WILF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11291))

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Abstract

We are currently experiencing the Big Data Era [20]: large volume of information is generated by different sources and may have different formats (variety) [5].

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References

  1. Ahmad, S.S.S., Pedrycz, W.: The development of granular rule-based systems: a study in structural model compression. Granular Comput. 2(1), 1–12 (2017)

    Article  Google Scholar 

  2. Al-Ali, A., Zualkernan, I.A., Rashid, M., Gupta, R., Alikarar, M.: A smart home energy management system using iot and big data analytics approach. IEEE Trans. Consum. Electron. 63(4), 426–434 (2017)

    Article  Google Scholar 

  3. Alonso, J.M., Castiello, C., Mencar, C.: Interpretability of fuzzy systems: current research trends and prospects. In: Kacprzyk, J., Pedrycz, W. (eds.) Springer Handbook of Computational Intelligence, pp. 219–237. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-43505-2_14

    Chapter  Google Scholar 

  4. Antonelli, M., Ducange, P., Marcelloni, F.: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Inf. Sci. 283, 36–54 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Anuradha, J., et al.: A brief introduction on big data 5Vs characteristics and hadoop technology. Procedia Comput. Sci. 48, 319–324 (2015)

    Article  Google Scholar 

  6. Bechini, A., Marcelloni, F., Segatori, A.: A MapReduce solution for associative classification of big data. Inf. Sci. 332, 33–55 (2016)

    Article  Google Scholar 

  7. Casalino, G., Castellano, G., Mencar, C.: Incremental adaptive semi-supervised fuzzy clustering for data stream classification. In: 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) (2018)

    Google Scholar 

  8. Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms: with Applications to Image Processing and Pattern Recognition. Advances in Fuzzy Systems - Applications and Theory, vol. 10. World Scientific, Singapore (1996)

    MATH  Google Scholar 

  9. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  10. Ducange, P., Marcelloni, F.: Multi-objective evolutionary fuzzy systems. In: Fanelli, A.M., Pedrycz, W., Petrosino, A. (eds.) WILF 2011. LNCS (LNAI), vol. 6857, pp. 83–90. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23713-3_11

    Chapter  Google Scholar 

  11. Ducange, P., Pecori, R., Mezzina, P.: A glimpse on big data analytics in the framework of marketing strategies. Soft Comput. 22(1), 325–342 (2018)

    Article  Google Scholar 

  12. Elkano, M., Galar, M., Sanz, J., Bustince, H.: CHI-BD: a fuzzy rule-based classification system for big data classification problems. Fuzzy Sets Syst. 348, 75–101 (2018)

    Article  MathSciNet  Google Scholar 

  13. Fazzolari, M., Alcalá, R., Nojima, Y., Ishibuchi, H., Herrera, F.: A review of the application of multi-objective evolutionary fuzzy systems: current status and further directions. IEEE Trans. Fuzzy Syst. 21(1), 45–65 (2013)

    Article  Google Scholar 

  14. Fernandez, A., Almansa, E., Herrera, F.: Chi-Spark-RS: an spark-built evolutionary fuzzy rule selection algorithm in imbalanced classification for big data problems. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)

    Google Scholar 

  15. Fernández, A., Carmona, C.J., del Jesus, M.J., Herrera, F.: A view on fuzzy systems for big data: progress and opportunities. Int. J. Comput. Intell. Syst. 9(sup1), 69–80 (2016)

    Article  Google Scholar 

  16. Fernández, A., del Río, S., Bawakid, A., Herrera, F.: Fuzzy rule based classification systems for big data with MapReduce: granularity analysis. Adv. Data Anal. Classif. 11(4), 711–730 (2017)

    Article  MathSciNet  Google Scholar 

  17. Ferranti, A., Marcelloni, F., Segatori, A., Antonelli, M., Ducange, P.: A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Inf. Sci. 415, 319–340 (2017)

    Article  Google Scholar 

  18. Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340–4360 (2011)

    Article  Google Scholar 

  19. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Data Management Systems, 3rd edn. Morgan Kaufmann, Waltham (2012)

    MATH  Google Scholar 

  20. John Walker, S.: Big Data: A Revolution that Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, Boston (2014)

    Google Scholar 

  21. Kim, Y., Shim, K., Kim, M.S., Lee, J.S.: DBCURE-MR: an efficient density-based clustering algorithm for large data using mapreduce. Inf. Syst. 42, 15–35 (2014)

    Article  Google Scholar 

  22. López, V., del Río, S., Benítez, J.M., Herrera, F.: Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets Syst. 258, 5–38 (2015)

    Article  MathSciNet  Google Scholar 

  23. Ludwig, S.A.: Mapreduce-based fuzzy c-means clustering algorithm: implementation and scalability. Int. J. Mach. Learn. Cybern. 6(6), 923–934 (2015)

    Article  Google Scholar 

  24. Maillo, J., Ramírez, S., Triguero, I., Herrera, F.: kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl.-Based Syst. 117, 3–15 (2017)

    Article  Google Scholar 

  25. Márquez, A., Márquez, F., Peregrín, A.: A scalable evolutionary linguistic fuzzy system with adaptive defuzzification in big data. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)

    Google Scholar 

  26. del Río, S., López, V., Benítez, J.M., Herrera, F.: A MapReduce approach to address big data classification problems based on the fusion of linguistic fuzzy rules. Int. J. Comput. Intell. Syst. 8(3), 422–437 (2015)

    Article  Google Scholar 

  27. Segatori, A., Bechini, A., Ducange, P., Marcelloni, F.: A distributed fuzzy associative classifier for big data. IEEE Trans. Cybern. 48(9), 2656–2669 (2018)

    Article  Google Scholar 

  28. Segatori, A., Marcelloni, F., Pedrycz, W.: On distributed fuzzy decision trees for big data. IEEE Trans. Fuzzy Syst. 26(1), 174–192 (2018)

    Article  Google Scholar 

  29. Wan, J., et al.: A manufacturing big data solution for active preventive maintenance. IEEE Trans. Ind. Inform. 13(4), 2039–2047 (2017)

    Article  Google Scholar 

  30. Wang, H., Xu, Z., Pedrycz, W.: An overview on the roles of fuzzy set techniques in big data processing: trends, challenges and opportunities. Knowl.-Based Syst. 118, 15–30 (2017)

    Article  Google Scholar 

  31. Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–361 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. SMART Engineering Solutions and Technologies (SMARTEST) Research Centre, eCAMPUS University, Via Isimbardi 10, 22060, Novedrate, Italy

    Pietro Ducange

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  1. Pietro Ducange
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Correspondence to Pietro Ducange .

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Editors and Affiliations

  1. Széchenyi István University, Győr, Hungary

    Robert Fullér

  2. Ca’ Foscari University of Venice, Venice, Italy

    Silvio Giove

  3. University of Genoa, Genoa, Italy

    Francesco Masulli

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Ducange, P. (2019). Fuzzy Models for Big Data Mining. In: Fullér, R., Giove, S., Masulli, F. (eds) Fuzzy Logic and Applications. WILF 2018. Lecture Notes in Computer Science(), vol 11291. Springer, Cham. https://doi.org/10.1007/978-3-030-12544-8_24

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  • DOI: https://doi.org/10.1007/978-3-030-12544-8_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12543-1

  • Online ISBN: 978-3-030-12544-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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