Skip to main content
SpringerLink
Log in

Association Rules in Data with Various Time Periods

  • Conference paper
  • First Online:
Man-Machine Interactions 5 (ICMMI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 659))

Included in the following conference series:

Abstract

In this paper an Association Rules data mining technique is adopted to explore the co-movement between sector indices listed on the Warsaw Stock Exchange. The indices are related to the various sectors of the economy. Because of the different time ranges the various indices are traded, the special approach has been used. That allowed us to analyze data in a wide range of time. The results were compared to those obtained using the tradi-tional approach. We observed higher values of measures and smaller errors for a majority of rules.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD 1993, pp. 207–216. ACM, Washington, D.C. (1993)

    Google Scholar 

  2. Agrawal, R., Shafer, J.: Parallel mining of association rules. IEEE Trans. Knowl. Data Eng. 8(6), 962–969 (1996)

    Article  Google Scholar 

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., Santiago (1994)

    Google Scholar 

  4. Azevedo, P., Jorge, A.: Comparing rule measures for predictive association rules. In: Kok, J., Koronacki, R., de Mántaras, J., Matwin, S., Mladenic, D., Skowron, A. (eds.) ECML 2007, vol. 4701, pp. 510–517. Springer, Warsaw (2007)

    Google Scholar 

  5. BOŚ SA: http://bassa.pl. Accessed 10 May 2016

  6. Karpio, K., Łukasiewicz, P., Orłowski, A., Zabkowski, T.: Mining associations on the Warsaw stock exchange. Acta Phys. Polonica A 123(3), 553–559 (2013)

    Article  Google Scholar 

  7. Karpio, K., Łukasiewicz, P., Orłowski, A.: Associations rules between sector indices on the Warsaw stock exchange. In: Řepa, V., Bruckner, T. (eds.) BIR 2016. LNBIP, vol. 261, pp. 312–321. Springer, Cham (2016)

    Chapter  Google Scholar 

  8. Na, S., Sohn, S.: Forecasting changes in Korea composite stock price index (KOSPI) using association rules. Expert Syst. Appl. 38, 9046–9094 (2011)

    Article  Google Scholar 

  9. Pan, Y., Haran, E., Manago, S., Hu, Y.: Co-movement of European stock markets based on association rule mining. In: ICDA 2014, pp. 54–58. IARIA XPS Press, Rome (2014)

    Google Scholar 

  10. Phan, N., Ienco, D., Malerba, D., Poncelet, P., Teisseire, M.: Mining multi-relational gradual patterns. In: SIAM 2015, pp. 846–854. SIAM Publications Online, Vancouver (2015)

    Google Scholar 

  11. Phan, N., Ienco, D., Poncelet, P., Teisseire, M.: Mining time relaxed gradual moving object clusters. In: ICAGIS 2012, pp. 478–481. ACM, Redondo Beach (2012)

    Google Scholar 

  12. Phan, N., Poncelet, P., Teisseire, M.: All in one: mining multiple movement patterns. Int. J. Inf. Technol. Decis. Mak. 15, 1115–1156 (2016)

    Article  Google Scholar 

  13. Srisawat, A.: An application of association rule mining based on stock market. In: ICMiA 2011, pp. 259–262 (2011)

    Google Scholar 

  14. Utthammajai, K., Leesutthipornchai, P.: Association mining on stock index indicators. Int. J. Comput. Commun. Eng. 4(1), 46–49 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Warsaw University of Life Sciences - SGGW, ul. Nowoursynowska 166, 02-787, Warsaw, Poland

    Krzysztof Karpio & Piotr Łukasiewicz

Authors
  1. Krzysztof Karpio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Łukasiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krzysztof Karpio .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Katarzyna Harezlak

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Piotrowska

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Karpio, K., Łukasiewicz, P. (2018). Association Rules in Data with Various Time Periods. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67792-7_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67791-0

  • Online ISBN: 978-3-319-67792-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation