Skip to main content
SpringerLink
Log in

Data Mining Techniques

  • Chapter
  • First Online:
Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

  • 547 Accesses

Abstract

This chapter presents available data mining techniques that can be of interest for application in indoor environment analysis. Descriptive statistics tools are presented with the focus on probability distribution and correlation analysis. Multivariate data techniques are also addressed, with a special focus on principal components determination and cluster analysis.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

  • Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. San Francisco: Morgan Kaufman, Elsevier.

    Google Scholar 

  • Haldar, A., & Mahadevan, S. (2000). Probability, reliability and statistical methods in engineering design. New York: Wiley.

    Google Scholar 

  • Montgomery, D., & Runger, G. (2010). Applied statistics and probability for engineers. New York: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Porto, Porto, Portugal

    Nuno M. M. Ramos

  2. Department of Civil Engineering, University of Porto, Porto, Portugal

    João M. P. Q. Delgado

  3. Department of Civil Engineering, Polytechnic Institute of Viseu, Viseu, Portugal

    Ricardo M. S. F. Almeida

  4. Department of Civil Engineering, University of Porto, Porto, Portugal

    Maria L. Simões

  5. Department of Civil Engineering, University of Porto, Porto, Portugal

    Sofia Manuel

Authors
  1. Nuno M. M. Ramos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João M. P. Q. Delgado
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricardo M. S. F. Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maria L. Simões
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sofia Manuel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nuno M. M. Ramos .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 The Author(s)

About this chapter

Cite this chapter

Ramos, N.M.M., Delgado, J.M.P.Q., Almeida, R.M.S.F., Simões, M.L., Manuel, S. (2016). Data Mining Techniques. In: Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-22294-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22294-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22293-6

  • Online ISBN: 978-3-319-22294-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation