Advertisement

An Improved Apriori Algorithm with Prejudging and Screening

  • Xuejian ZhaoEmail author
  • Dongjun Li
  • Yuan Yuan
  • Zhixin Sun
  • Yong Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)

Abstract

Association rule analysis, as one of the significant means of data mining, plays an important role in discovering the implicit knowledge in massive transaction data. Aiming at the inherent defects of the classic Apriori algorithm, this paper proposes IAPS (Improved Apriori with Prejudging and Screening) algorithm. IAPS algorithm adds a prejudging and screening procedure on the basis of the self-join and pruning progress in Apriori algorithm which can reduce and optimize the k-frequent item sets using prior probability. IAPS algorithm simplifies the operation process of mining frequent item sets. Experimental results show that the improved algorithm can effectively reduce the number of scanning databases and reduce the running time of the algorithm.

Keywords

Association rules Transaction database Prejudging and screening Apriori 

Notes

Acknowledgements

 Project supported by the National Natural Science Foundation of China under Grant nos. 61373135, 61300240, 61401225, 61502252; the Natural Science Foundation of Jiangsu Province of China under Grant nos. BK20140883, BK20140894, BK20131377; China Postdoctoral Science Foundation funded project under Grant no. 2015M581844; Jiangsu Planned Projects for Postdoctoral Research Funds under Grant no. 1501125B; NUPTSF under Grant no. NY214101, NY215147.

References

  1. 1.
    Singla, S., Malik, A.: Survey on various improved Apriori algorithms. International Journal of Advanced Research in Computer and Communication Engineering. 3(11), 8528–851 (2014).Google Scholar
  2. 2.
    Minal, G.I., Suryavanshi, N.Y.: Association rule mining using improved Apriori algorithm. International Journal of Computer Applications. 112(4), 37–42 (2015).Google Scholar
  3. 3.
    Rajeswari, K.: Improved Apriori algorithm – a comparative study using different objective measures. International Journal of Computer Science and Information Technologies. 6(3), 3185–3191 (2015).Google Scholar
  4. 4.
    Achar, A., Laxman, S., Sastry, P.S.: A unified view of the Apriori-based algorithms for frequent episode discovery. Knowledge & Information Systems. 31(2), 223–250 (2012).Google Scholar
  5. 5.
    Peng, L., Xiaoyang, Y., Boyu, S.: Video recommendation method based on group user behavior analysis. Journal of Electronics & Information Technology. 36(6), 1484–1491 (2014).Google Scholar
  6. 6.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB ‘94 Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Santiago (1994).Google Scholar
  7. 7.
    Yang, Z., Tang, W., Shintemirov, A., et al.: Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 39(6):597–610 (2009).Google Scholar
  8. 8.
    Zhang, F., Zhang, Y., Bakos, J.D.: Gpapriori: Gpu-accelerated frequent itemset mining. In: 2011 IEEE International Conference on Cluster Computing, pp. 590–594, Austin, TX, USA (2011).Google Scholar
  9. 9.
    Angeline, M.D., James S.P.: Association rule generation using Apriori mend algorithm for student’s placement. International Journal of Emerging Sciences. 2(1),78–86 (2012).Google Scholar
  10. 10.
    Li, N., Zeng, L., He, Q.: Parallel implementation of apriori algorithm based on MapReduce. In: 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing (SNPD), pp. 236–241(2012).Google Scholar
  11. 11.
    Sulianta, F., Liong, T.H., Atastina, I.: Mining food industry’s multidimensional data to produce association rules using Apriori algorithm as a basis of business strategy. In: 2013 International Conference of Information and Communication Technology (ICoICT), pp. 176–181, Bandung, 2013.Google Scholar
  12. 12.
    Lin, G., Xinsheng, J., Tao J.: Discovery of network information content security incidents based on association rules and its implementation in Map-Reduce. Journal of Electronics & Information Technology. 36(8), 1831–1837(2014).Google Scholar
  13. 13.
    Rao, S., Gupta, R.: Implementing improved algorithm over Apriori data mining association rule algorithm. International Journal of Computer Science and Technology. 34(3), 489–493(2012).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Xuejian Zhao
    • 1
    • 2
    Email author
  • Dongjun Li
    • 1
  • Yuan Yuan
    • 2
  • Zhixin Sun
    • 1
  • Yong Chen
    • 3
  1. 1.Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of EducationNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Jiangsu Posts & Telecommunications Planning and Designing Institute Co. LTDNanjingChina
  3. 3.Nanjing Longyuan Microelectronic Co. LTDNanjingChina

Personalised recommendations