Nothing Special   »   [go: up one dir, main page]

Skip to main content

Data Association Rules in Analyzing Performance Level of College Students

  • Conference paper
Applied Informatics and Communication (ICAIC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 226))

Included in the following conference series:

  • 1583 Accesses

Abstract

Relation itemsets can be found by association rules of data mining. This article uses association rules of Apriori algorithm and analyzes performance levels of college students. Valuable rules and information are obtained, so a fundamental improvement is made in students’ study.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of ACM SIGMOD Conference on Management of Data, pp. 207–216. ACM, New York (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)

    Google Scholar 

  3. Han, J.-w., Micheline, K.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)

    MATH  Google Scholar 

  4. Wang, Y.: Data mining of association rules. Chengdu University of Information Technology 2(19), 173 (2004)

    Google Scholar 

  5. Mao, G., Duan, L., Wang, S., Shi, Y.: Data mining principles and algorithms, pp. 68–72. Tsinghua University Press, Beijing (2007)

    Google Scholar 

  6. Xu, L.: The practical application of association rules Apriori algorithm. Computer Knowledge and Technology, 862–864 (2008)

    Google Scholar 

  7. Dong, P.: Association rules in student achievement. Sanmenxia Vocational and Technical College 8(4), l17M20 (2009)

    Google Scholar 

  8. Zheng, C., Han, C., Dong, J.: Association Rules in the teaching evaluation. Computer Technology and Development 19(9), 215–217 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhong, R., Wang, H. (2011). Data Association Rules in Analyzing Performance Level of College Students. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23235-0_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23235-0_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23234-3

  • Online ISBN: 978-3-642-23235-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics