Skip to main content
SpringerLink
Log in

Mining Frequents Itemset and Association Rules in Diabetic Dataset

  • Conference paper
  • First Online:
Business Intelligence (CBI 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 449))

Included in the following conference series:

  • 506 Accesses

Abstract

Data mining is a field of science to extract and analyses the information from large dataset. One of the most techniques is association rule mining. It aim is to find the relationship between the different attributes of data. Several algorithms for extracting data have been developed. Among the existing algorithms the FP-Growth algorithm is one of well-know algorithm in finding out the desired association rules. The aim of this paper is the extraction of association rules by FP-Growth algorithm and its variants using a diabetic dataset, which are the CFP-Growth and ICFP-Growth. Experimental results show that the ICFP-Growth is more accurate than CFP-Growth and FP-Growth.

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data - SIGMOD 2000, pp. 1–12. Dallas, Texas, United States (2000)

    Google Scholar 

  2. Fakir, Y., et al.: A comparative study between Relim and SaM algorithms. Int. J. Comput. Sci. Inf. Secur. 18(5) (2020)

    Google Scholar 

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

    Google Scholar 

  4. Joshua, J.V., et al.: Data mining: a book recommender system using frequent pattern algorithm. J. Softw. Eng. Simul. 3(3), 01–13 (2016)

    Google Scholar 

  5. Pathania, S., Singh, H.: A new associative classifier based on CFP-Growth++ algorithm. In: Proceedings of the Sixth International Conference on Computer and Communication Technology 2015, pp. 20–25. New York, NY, USA (2015)

    Google Scholar 

  6. Uday Kiran, R., Krishna Reddy, P.: Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: Proceedings of the 14th International Conference on Extending Database Technology - EDBT/ICDT 2011, p. 11. Uppsala, Sweden (2011)

    Google Scholar 

  7. Fakir, Y., et al.: Closed frequent itemsets mining based on It-Tree. Global J. Comput. Sci. Theory Res. 11(1) (2021)

    Google Scholar 

  8. Fakir, Y., El Ayachi, R., Fakir, M.: Mining frequent pattern by titanic and FP-Tree algorithms. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 6(5) (2020)

    Google Scholar 

  9. Uday Kiran, R., Krishna Reddy, P.: An improved frequent pattern-growth approach to discover rare association rules. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 43–52. Funchal - Madeira, Portugal (2009)

    Google Scholar 

  10. Liu, G., et al.: CFP-tree: a compact disk-based structure for storing and querying frequent itemsets. Inf. Syst. 32(2), 295–319 (2007)

    Google Scholar 

  11. Darraba, S., Ergenc, B.: Vertical pattern mining algorithm for multiple support thresholds. In: International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2017, 6–8 September 2017, Marseille, France (2017)

    Google Scholar 

  12. Ahmed, S.A., Nath, B.: ISSP-tree: an improved fast algorithm for constructing a complete prefix tree using single database scan. Expert Syst. Appl. 185, 115603 (2021)

    Google Scholar 

  13. Gan, W.: Mining of frequent patterns with multiple minimum supports. Eng. Appl. Artif. Intell. 60, 83–96 (2017)

    Google Scholar 

  14. Chen, X., Huang, H., Du, W.: Most frequent item sets mining algorithm based on MIS-Tree and multiple support array. Int. J. Res. Eng. Sci. 4(7) (2016)

    Google Scholar 

  15. Sinthuja, M., Rachel, S.S., Janani, G.: Mis-Tree algorithm for mining association rules with multiple minimum support. Bonfring Int. J. Datamin. 1, 1–5 (2021)

    Google Scholar 

  16. Fakir, Y., El Ayachi, R.: Frequent patterns mining. Int. J. Sci. Res. Comput. Sc. Eng. Inf. Technol. 6(4) (2020)

    Google Scholar 

  17. Narvekara, M., Syed, S.F.: An optimized algorithm for association rule mining using FP tree. In: International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), Procedia Computer Science, vol. 45, 101–110 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Youssef Fakir, Abdelfatah Maarouf & Rachid El Ayachi

Authors
  1. Youssef Fakir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelfatah Maarouf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachid El Ayachi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youssef Fakir .

Editor information

Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  2. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Baslam

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fakir, Y., Maarouf, A., El Ayachi, R. (2022). Mining Frequents Itemset and Association Rules in Diabetic Dataset. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06458-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06457-9

  • Online ISBN: 978-3-031-06458-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation