Skip to main content
SpringerLink
Log in

Online Shopping Customer Data Analysis by Using Association Rules and Cluster Analysis

  • Conference paper
Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7987))

Included in the following conference series:

Abstract

Data Mining is the process of exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. Data mining is considered as the only solution towards efficient use of increasing amounts of data worldwide. The process of converting data into information is achieved by means of data mining. In this study, first the concept of data mining is presented, then CRISP-DM process are described. In this paper Cluster Analysis and Association Rules are used to analyze the data. k-means Algorithm, Confidence and Support Ratios are theoretically explained and these techniques applied to a data set obtained from 314 customers from 7 regions of Turkey to identify their profile.

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 PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. Artificial Intelligence in Medicine 26(1-2), 175–178 (2002)

    Article  Google Scholar 

  2. Berry, M.J.A., Linoff, G.S.: Mastering Data Mining, p. 7. Wiley (2000)

    Google Scholar 

  3. Giudici, P.: Applied Data Mining, p. 1. Wiley & Sons (2003)

    Google Scholar 

  4. Data Mining Processes (December 6, 2012), http://www.zentut.com/data-mining/data-mining-processes/

  5. Han, J., Kamber, M.: Data Mining Concepts and Techniques, p. 228. Morgan Kaufmann (2006)

    Google Scholar 

  6. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, pp. 327–497. Pearson Education (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Business Administration, Istanbul University, Istanbul, Turkey

    Serhat Güden & Umman Tugba Gursoy

Authors
  1. Serhat Güden
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Umman Tugba Gursoy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Güden, S., Gursoy, U.T. (2013). Online Shopping Customer Data Analysis by Using Association Rules and Cluster Analysis. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2013. Lecture Notes in Computer Science(), vol 7987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39736-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39736-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39735-6

  • Online ISBN: 978-3-642-39736-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation