Skip to main content
SpringerLink
Log in

Mining Frequent Itemset Using Quine–McCluskey Algorithm

  • Conference paper
  • First Online:
Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

  • 1302 Accesses

Abstract

This paper presents an approach which uses Quine–McCluskey algorithm in order to discover frequent itemsets to generate association rules. In this approach, the given transaction database is converted into a Boolean matrix form to discover frequent itemsets. After generating the Boolean matrix of given database Quine–McCluskey algorithm is applied. Quine–McCluskey algorithm minimizes the given Boolean matrix to generate the frequent itemset pattern. This method requires less number of scans compared to other existing techniques.

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. Han, I., Kamber, M.: Data Mining concepts and Techniques, pp. 335–389. M. K. Publishers (2000)

    Google Scholar 

  2. Agrawal, R., Imielinski, T., Swami, T.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 207–216. Washington, D.C. (1993)

    Google Scholar 

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference. Santiago, Chile (1994)

    Google Scholar 

  4. Srikant, R., Agrawal, R.: Mining Generalized Association Rules, pp. 407–419 (1999)

    Google Scholar 

  5. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM Press (2000)

    Google Scholar 

  6. Lin, Y.C., Hung, C.M., Huang, Y.M.: Mining ensemble association rules by Karnaugh Map. In: World Congress on Computer Science and Information Engineering, pp. 320–324 (2009)

    Google Scholar 

  7. Sharma, N., Singh, A.: K-partition for mining frequent patterns in larger databases. Int. J. Comput. Sci. Eng. (IJCSE). 4(09), (2012). ISSN:0975-3397

    Google Scholar 

  8. Mandrai, P., Baskar, R.: A novel algorithm for optimization of association rule using k-map and genetic algorithm. Int. Conf. Comput. Commun. Netw. Technol. 1–7 (2013)

    Google Scholar 

  9. Ravi, C., Khare, N.: EO-ARM: an efficient and optimized k-map based positive-negative association rule mining technique. Int. Conf. Circuits Power Comput. Technol. 1723–1727 (2014)

    Google Scholar 

  10. ‎McCluskey, E.‎J.: Minimization‎ of ‎boolean ‎functions. ‎Bell ‎Syst.‎ Tech. ‎J. 35(5), 1417–1444 (1956)

    Google Scholar 

  11. ‎Quine,‎ W.V.: ‎The ‎problem ‎of ‎simplifying ‎truth‎ functions. ‎Am. ‎Math. ‎Montly 59(8), 521–31 (1952)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graphic Era Hill University, Dehradun, India

    Kanishka Bajpayee & Shashi Kumar Sharma

  2. IIT Roorkee, Roorkee, India

    Surya Kant

  3. Graphic Era University, Dehradun, India

    Bhaskar Pant &  Ankur Chaudhary

Authors
  1. Kanishka Bajpayee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surya Kant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bhaskar Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ankur Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shashi Kumar Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surya Kant .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Kanishka Bajpayee, Surya Kant, Bhaskar Pant, Ankur Chaudhary, Sharma, S.K. (2016). Mining Frequent Itemset Using Quine–McCluskey Algorithm. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_68

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0451-3_68

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation