Skip to main content

A New Approach for Problem of Sequential Pattern Mining

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Duraiswamy, K., Jayanthi, B.: A New Approach to Discover Periodic Frequent Patterns. Computer and Information Science 4(2) (March 2011)

    Google Scholar 

  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. Kaneiwa, K., Kudo, Y.: A Sequential Pattern Mining Algorithm using Rough Set Theory. International Journal of Approximate Reasoning 52(6), 894–913 (2011)

    CrossRef  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. 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)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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. 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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  20. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. The Morgan Kaufmann Publishers (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nguyen, TT., Nguyen, PK. (2012). A New Approach for Problem of Sequential Pattern Mining. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34630-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34629-3

  • Online ISBN: 978-3-642-34630-9

  • eBook Packages: Computer ScienceComputer Science (R0)