Advertisement

Mining Maximal Association Rules on Soft Sets Using Critical Relative Support Based Pruning

  • Uddagiri ChandrasekharEmail author
  • G. Vaishnavi
  • D. Lakshmi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

The paper proposes a modification of the Apriori algorithm called Maximal Association rules that combines the ability to mine rules that are lost in regular mining and the speed and efficient memory usage of the soft-set based scheme. Not only can this improve the efficiency without sacrificing a lot of accuracy but it also makes the Apriori Algorithm capable of handling uncertainty in data. Association rules were pruned on a soft set based information system using CRSthreshold. The combination was found useful especially in text mining.

Keywords

Soft sets Maximal support Critical relative support Association rule mining 

References

  1. 1.
    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, VLDB, Santiago, Chile, pp. 487–499, September 1994Google Scholar
  2. 2.
    Abdullah, Z., Herawan, T., Ahmad, N., Deris, M.M.: Mining significant association rules from educational data using critical relative support approach. Proc. Soc. Behav. Sci. 28, 97–101 (2011)CrossRefGoogle Scholar
  3. 3.
    Molodtsov, D.: Soft set theory-first results. Comput. Math Appl. 37, 19–31 (1999)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Çağman, N., Çıtak, F., Enginoğlu, S.: Fuzzy parameterized fuzzy soft set theory and its applications. TJFS: Turk. J. Fuzzy Syst. 1(1), 21–35 (2010)zbMATHGoogle Scholar
  5. 5.
    Herawan, T., Deris, M.M.: A soft set approach for association rules mining. Knowl.-Based Syst. 24, 186–195 (2011)CrossRefGoogle Scholar
  6. 6.
    Kanojiya, S.S., Tiwari, A.: A new soft set based association rule mining algorithm. TECHNIA–Int. J. Comput. Sci. Commun. Technol 6(2), 948 (2014)Google Scholar
  7. 7.
    Rahman, C.M., Sohel, F.A., Naushad, P., Kamruzzaman, S.M.: Text classification using the concept of association rule of data mining. CoRR, abs/1009.4582 (2010)Google Scholar
  8. 8.
    Saraf, S., Adlakha, N., Sharma, S.: Absolute soft set approach for mining association patterns. Int. J. Comput. Appl. 84(4), 35–39 (2013)CrossRefGoogle Scholar
  9. 9.
    Rose, A.N.M., Awang, M.I., Hassan, H., Deris, M.M.: Comparison of techniques in solving incomplete datasets in softest. Int. J. Database Theory Appl. 4(3) (2011)Google Scholar
  10. 10.
    Kanojiya, S.S., Tiwari, A.: A new soft set based association rule mining. Technia 6(2), 948 (2014). ISSN 0974-3375Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Uddagiri Chandrasekhar
    • 1
    Email author
  • G. Vaishnavi
    • 1
  • D. Lakshmi
    • 2
  1. 1.Department of Computer Science and EngineeringBVIRT HyderabadHyderabadIndia
  2. 2.Department of Computer Science and EngineeringBV Raju Institute of TechnologyNarsapur, MedakIndia

Personalised recommendations