Skip to main content
SpringerLink
Log in

Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

The basic idea of Apriori algorithm is first introduced in this paper, which is to find all frequent sets in a transaction. The frequent requirements of these frequent sets are greater than or equal to the minimum support of the set. On this basis, the working principle of the traditional Apriori algorithm is analyzed, and the existing problems are pointed out. To solve these problems, an improved Apriori algorithm is proposed for time series of frequent itemsets. Finally, on the basis of analyzing the methods and processes of mining association rules for time series, this improved time series Apriori algorithm for frequent itemsets is applied to mining association rules based on time constraints. The experimental results show that the improved Apriori algorithm is better than the traditional one in storage space.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Han J, Pei J, Yan X (2004) From sequential pattern mining to structured pattern mining: a pattern-growth approach. J Comput Sci Technol 19:257–279

    Article  MathSciNet  Google Scholar 

  2. Pei J, Han J-W, Mortazavi-Asl B et al (2004) Mining sequential pattern by pattern-growth: the prefix span approach. IEEE TKDE 16(10):9–13

    Google Scholar 

  3. Li C, Yang X (2012) The research of recommendation systems in e-commerce based on association rule improved-Apriori algorithms. IJACT 4(21):354–361

    Article  Google Scholar 

  4. Han J, Kamber M (2012) Data mining: concepts and techniques. China Machine Press, Beijing, p 160166

    MATH  Google Scholar 

  5. Guodong L, Kewen X (2011) An improved data mining technique combined Apriori algorithm with ant colony algorithm and its application. JDCTA 5(8):241–249

    Article  Google Scholar 

  6. Zubcoff J, Trujillo J (2007) A UML 2.0 profile to design Association Rule mining models in the multidimensional conceptual modeling of data warehouses. Data Knowl Eng 63(1):44–62

    Article  Google Scholar 

  7. Chen G, Liu H, Yu L, Wei Q, Zhang X (2006) A new approach to classification based on association rule mining. Decis Support Syst 42(2):674–689

    Article  Google Scholar 

  8. Hu Y-H, Chen Y-L (2006) Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decis Support Syst 42(1):1–24

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by Scientific and Technological Project of Henan Province (No. 182102210486), Key Scientific Research Project of University in Henan Province (No. 18A520008).

Author information

Authors and Affiliations

  1. School of Information Technology, Shangqiu Normal University, Shangqiu, 476000, Henan, China

    Chunxia Wang & Xiaoyue Zheng

Authors
  1. Chunxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyue Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunxia Wang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Zheng, X. Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint. Evol. Intel. 13, 39–49 (2020). https://doi.org/10.1007/s12065-019-00234-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00234-5

Keywords

Search

Navigation