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A New Approach for Problem of Sequential Pattern Mining

  • Thanh-Trung Nguyen
  • Phi-Khu Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7653)

Abstract

Frequent Pattern Mining is an important data mining task and it has been a focus theme in data mining research. One of the main issues in Frequent Pattern Mining is Sequential Pattern Mining retrieved the relationships among objects in sequential dataset. AprioriAll is a typical algorithm to solve the problem in Sequential Pattern Mining but its complexity is so high and it is difficult to apply in large datasets. Recently, to overcome the technical difficulty, there are a lot of researches on new approaches such as custom-built Apriori algorithm, modified Apriori algorithm, Frequent Pattern-tree and its developments, integrating Genetic algorithms, Rough Set Theory or Dynamic Function to solve the problem of Sequential Pattern Mining. However, there are still some challenging research issues that time consumption is still hard problem in Sequential Pattern Mining. This paper introduces a new approach with a model presented with definitions and operations. The proposed algorithm based on this model finds out the sequential patterns with quadratic time to solve absolutely problems in Sequential Pattern Mining and significantly improve the speed of calculation and data analysis.

Keywords

AprioriAll popular element probability sequential pattern mining 

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References

  1. 1.
    Duraiswamy, K., Jayanthi, B.: A New Approach to Discover Periodic Frequent Patterns. Computer and Information Science 4(2) (March 2011)Google Scholar
  2. 2.
    Deypir, M., Sadreddini, M.H.: An Efficient Algorithm for Mining Frequent Itemsets Within Large Windows Over Data Streams. International Journal of Data Engineering (IJDE) 2(3) (2011)Google Scholar
  3. 3.
    Kaneiwa, K., Kudo, Y.: A Sequential Pattern Mining Algorithm using Rough Set Theory. International Journal of Approximate Reasoning 52(6), 894–913 (2011)CrossRefGoogle Scholar
  4. 4.
    Sharma, H., Garg, D.: Comparative Analysis of Various Approaches Used in Frequent Pattern Mining. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence IJACSA, 141–147 (August 2011)Google Scholar
  5. 5.
    Prasad, K.S.N., Ramakrishna, S.: Frequent Pattern Mining and Current State of the Art. International Journal of Computer Applications (0975 - 8887) 26(7) (July 2011)Google Scholar
  6. 6.
    Vijaya Prakash, R., Govardhan, Sarma, S.S.V.N.: Mining Frequent Itemsets from Large Data Sets using Genetic Algorithms. IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications, AIT (2011)Google Scholar
  7. 7.
    Uday Kiran, R., Krishna Reddy, P.: Novel Techniques to Reduce Search Space in Multiple Minimum Supports-Based Frequent Pattern Mining Algorithms. In: Proceeding EDBT/ICDT 2011 Proceedings of the 14th International Conference on Extending Database Technology, Uppsala, Sweden, March 22-24 (2011)Google Scholar
  8. 8.
    Joshi, S., Jadon, R.S., Jain, R.C.: An Implementation of Frequent Pattern Mining Algorithm using Dynamic Function. International Journal of Computer Applications (0975-8887) 9(9) (November 2010)Google Scholar
  9. 9.
    Rawat, S.S., Rajamani, L.: Discovering Potential User Browsing Behaviors Using Custom-Built Apriori Algorithm. International Journal of Computer Science & Information Technology (IJCSIT) 2(4) (August 2010)Google Scholar
  10. 10.
    Goswami, D.N., Chaturvedi, A., Raghuvanshi, C.S.: Frequent Pattern Mining Using Record Filter Approach. IJCSI International Journal of Computer Science Issues 4(7) (July 2010)Google Scholar
  11. 11.
    Raghunathan, A., Murugesan, K.: Optimized Frequent Pattern Mining for Classified Data Sets. International Journal of Computer Applications (0975 - 8887) 1(27) (2010)Google Scholar
  12. 12.
    Zheng, Z., Zhao, Y., Zuo, Z., Cao, L.: An Efficient GA-Based Algorithm for Mining Negative Sequential Patterns. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS, vol. 6118, pp. 262–273. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Bahel, M., Dule, C.: Analysis of Frequent Itemset generation process in Apriori and RCS (Reduced Candidate Set) Algorithm. Int. J. Advanced Networking and Applications 02(02), 539–543 (2010)Google Scholar
  14. 14.
    Chen, Y.-L., Kuo, M.-H., Wu, S.-Y., Tang, K.: Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications 8, 241–251 (2009)CrossRefGoogle Scholar
  15. 15.
    Chang, L., Wang, T., Yang, D., Luan, H., Tang, S.: Efficient algorithms for incremental maintenance of closed sequential patterns in large databases. Data & Knowledge Engineering 68, 68–106 (2009)CrossRefGoogle Scholar
  16. 16.
    Ma, Z., Xu, Y., Dillon, T.S., Xiaoyun, C.: Mining Frequent Sequences Using Itemset-Based Extension. In: Proceedings of International MultiConference of Engineers and Computer Scientists (IMECS 2008), Hong Kong, March 19-21, vol. 1 (2008)Google Scholar
  17. 17.
    Saputra, D., Rambli, D.R.A., Foong, O.M.: Mining Sequential Patterns Using I-PrefixSpan. Proceedings of World Academy of Science, Engineering and Technology 26 (December 2007)Google Scholar
  18. 18.
    Cavique, L.: A Network Algorithm to Discover Sequential Patterns. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 406–414. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Yang, Z., Wang, Y., Kitsuregawa, M.: LAPIN: Effective Sequential Pattern Mining Algorithms by Last Position Induction for Dense Databases. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 1020–1023. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. The Morgan Kaufmann Publishers (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Thanh-Trung Nguyen
    • 1
  • Phi-Khu Nguyen
    • 1
  1. 1.Department of Computer Science, University of Information TechnologyVietnam National University HCM CityVietnam

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