Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Fuzzy Transform for Analyzing Massive Datasets

  • Chapter
  • First Online:
Fuzzy Transforms for Image Processing and Data Analysis

  • 335 Accesses

Abstract

By massive data or big data, we mean a collection of data so extensive in terms of volume, speed of generation, and acquisition and heterogeneity that it requires specific techniques and methods to be managed and explored.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified Data Processing on Large Clusters. In OSDI’04: Sixth Symposium on Operating System Design and Implementation (pp. 137–150). San Francisco, CA.

    Google Scholar 

  2. Wang, J., Liu, W., Kumar, S., & Chang, S.-F. (2015). Learning to hash for indexing big data: A survey. Proceedings of the IEEE, 104(1), 34–57. https://doi.org/10.1109/JPROC.2015.2487976.

    Article  Google Scholar 

  3. Laney, D. (2001). 3D data management: controlling data volume, velocity and variety. Application Delivery Strategies Meta Group, 949, 4.

    Google Scholar 

  4. Zikopoulos, P., Eaton, C., De Roos, D., Deutsch, T., & Lapis G. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (p. 166). McGraw Hill Professional. ISBN: 978-0-07-179053-6.

    Google Scholar 

  5. Chen, C. L. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Information Sciences, 275, 314–347.

    Article  Google Scholar 

  6. Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1). https://doi.org/10.1177/2053951716631130.

  7. Huber, P. (1997). Massive data sets workshop: The morning after. In J. K. Washington & D. Pregibon (Eds.), Massive Data Sets, Proceedings of a Workshop (pp. 169–184). Washington DC: National Academy Press.

    Google Scholar 

  8. Hathaway, R., & Bezdek, J. (2006). Extending fuzzy and probabilistic clustering to very large data sets. Computational Statistics & Data Analysis, 51, 215–234.

    Article  MathSciNet  Google Scholar 

  9. Wittek, P., (2014). Quantum Machine Learning. What Quantum Computing Means to Data Mining (p. 219). Academic Press. ISBN: 9780128009536.

    Google Scholar 

  10. Schuld, M., & Petruccione, F. (2014). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172–185.

    Article  Google Scholar 

  11. Schuld, M., & Petruccione, F. (2018). Supervised learning with quant.um computers. Quantum Science and Technology, 287. https://doi.org/10.1007/978-3-319-96424-9. ISBN: 978-3-319-96423-2.

  12. Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends in Signal Processing, 7(3–4), 1–199. https://doi.org/10.1561/2000000039.

  13. Lu, J., & LiBias, D. (2013). Correction in a small sample from big data. IEEE Transactions on Knowledge and Data Engineering, 25(11), 2658–2663.

    Article  Google Scholar 

  14. Kim, J. K., & Wang, Z. (2019). Sampling techniques for big data analysis. International Statistical Review, 87(S1), S177–S191.

    Google Scholar 

  15. Jun, S., Lee, S. J., & Ryu, Y. B. (2015). A divided regression analysis for big data. International Journal of Software Engineering and Its Applications, 9(5), 21–32.

    Google Scholar 

  16. Perfilieva, I., Novàk, V., & Dvoràk, A. (2008). Fuzzy transforms in the analysis of data. International Journal of Approximate Reasoning, 48, 36–46.

    Article  Google Scholar 

  17. Di Martino, F., Loia, V., & Sessa, S. (2010). Fuzzy transforms for compression and decompression of color videos. Information Sciences, 180, 3914–3931.

    Article  MathSciNet  Google Scholar 

  18. Segata, N., & Blanzieri, E. (2009). Fast local support vector machines for large datasets. In P. Perner (Eds.), Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science, (Vol. 5632, pp. 295–310). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-03070-3_22.

  19. Cheng, C. H., Tan, P., & Jin, R. (2010). Efficient algorithm for localized support vector machine. IEEE Transactions on Knowledge and Data Engineering, 22(4), 537–549.

    Article  Google Scholar 

  20. Zheng, J., Shen, F., Fan, H., & Zhao, J. (2013). An online incremental learning support vector machine for large-scale data. Neural Computing Applications, 22(5), 1023–1035.

    Article  Google Scholar 

  21. Peng H., Choi D., & Liang C. (2013). Evaluating parallel logistic regression models. In 2013 IEEE International Conference on Big Data. Silicon Valley, CA, USA, 6–9 Oct 2013. https://doi.org/10.1109/bigdata.2013.6691743.

  22. Huang, G.-B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: A survey. International Journal of Machine Learning and Cybernetics, 2(2), 107–122.

    Article  Google Scholar 

  23. He, Q., Shang, T., Zhuang, F., & Shi, Z. (2013). Parallel extreme learning machine for regression based on MapReduce. Neurocomputing, 102, 52–58.

    Article  Google Scholar 

  24. Chen, C., Li, K., Duan, M., & Li, K. (2017). Chapter 6: Extreme learning machine and its applications in big data processing. In Big Data Analytics for Sensor-Network Collected Intelligence (pp. 117–150). Intelligent Data-Centric Systems. https://doi.org/10.1016/B978-0-12-809393-1.00006-4.

  25. Yao, L., & Ge, Z. (2019). Distributed parallel deep learning of Hierarchical Extreme Learning Machine for multimode quality prediction with big process data. Engineering Applications of Artificial Intelligence, 81, 450–465.

    Article  Google Scholar 

  26. Di Martino, F., & Sessa, S. (2020). Attribute Dependency Data Analysis For Massive Datasets By Fuzzy Transforms. Soft Computing (in press).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Architecture, University of Naples Federico II, Naples, Italy

    Ferdinando Di Martino

  2. Department of Architecture, University of Naples Federico II, Naples, Italy

    Salvatore Sessa

Authors
  1. Ferdinando Di Martino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salvatore Sessa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ferdinando Di Martino .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Di Martino, F., Sessa, S. (2020). Fuzzy Transform for Analyzing Massive Datasets. In: Fuzzy Transforms for Image Processing and Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-44613-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-44613-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44612-3

  • Online ISBN: 978-3-030-44613-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation