Skip to main content
SpringerLink
Log in

Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data

  • Conference paper
  • First Online:
Discovery Science (DS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12323))

Included in the following conference series:

Abstract

Route mining from trajectory databases, or trajectory data mining, has become an important and valuable task since the popularization of GPS devices. Sequential pattern mining based approaches are well applied to trajectory data mining, while they often suffer the problem of high redundancy and low comprehensibility. In this paper, we solve this problem by proposing a novel approach of disjoint sequential pattern pair mining, which takes on a new perspective to this problem by focusing on extracting extra valuable information, i.e., hyper patterns from the “redundant” patterns instead of just removing them. We conduct experiments on a real tourist trajectory database as well as an artificial one. We show the practical applicability of our approach and the effectiveness and efficiency of our mining algorithm by analyzing the mining results.

Supported by CREST and North Grid Co., Ltd.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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

Notes

  1. 1.

    https://github.com/DSPPM/Disjoint-Sequential-Pattern-Pair-Mining/blob/master/all_trans_lv3.csv.

  2. 2.

    https://github.com/DSPPM/Disjoint-Sequential-Pattern-Pair-Mining/blob/master/dummy_trans_lv3.csv.

  3. 3.

    https://github.com/DSPPM/Disjoint-Sequential-Pattern-Pair-Mining.

References

  1. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE (1995)

    Google Scholar 

  2. Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Trans. Intell. Transp. Syst. 11(3), 647–657 (2010)

    Article  Google Scholar 

  3. Bermingham, L., Lee, I.: Mining distinct and contiguous sequential patterns from large vehicle trajectories. Knowl.-Based Syst. 189, 105076 (2020)

    Article  Google Scholar 

  4. Bin, C., Gu, T., Sun, Y., Chang, L.: A personalized poi route recommendation system based on heterogeneous tourism data and sequential pattern mining. Multimedia Tools Appl. 78(24), 35135–35156 (2019)

    Article  Google Scholar 

  5. Chen, L., Lv, M., Ye, Q., Chen, G., Woodward, J.: A personal route prediction system based on trajectory data mining. Inf. Sci. 181(7), 1264–1284 (2011)

    Article  Google Scholar 

  6. Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)

    Google Scholar 

  7. Han, J., et al.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224. Citeseer (2001)

    Google Scholar 

  8. Ibrahim, R., Shafiq, M.O.: Detecting taxi movements using random swap clustering and sequential pattern mining. J. Big Data 6(1), 39 (2019)

    Article  Google Scholar 

  9. Parent, C., et al.: Semantic trajectories modeling and analysis. ACM Comput. Surv. (CSUR) 45(4), 1–32 (2013)

    Article  Google Scholar 

  10. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0014140

    Chapter  Google Scholar 

  11. Takimoto, Y., Sugiura, K., Ishikawa, Y.: Extraction of frequent patterns based on users’ interests from semantic trajectories with photographs. In: Proceedings of the 21st International Database Engineering & Applications Symposium, pp. 219–227 (2017)

    Google Scholar 

  12. Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 25–34 (2014)

    Google Scholar 

  13. Yan, X., Han, J., Afshar, R.: Clospan: mining: closed sequential patterns in large datasets. In: Proceedings of the 2003 SIAM International Conference on Data Mining, pp. 166–177. SIAM (2003)

    Google Scholar 

  14. Yang, C., Gidófalvi, G.: Mining and visual exploration of closed contiguous sequential patterns in trajectories. Int. J. Geogr. Inf. Sci. 32(7), 1282–1304 (2018)

    Article  Google Scholar 

  15. Zhang, J., Wang, Y., Yang, D.: CCspan: mining closed contiguous sequential patterns. Knowl.-Based Syst. 89, 1–13 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Siqi Peng & Akihiro Yamamoto

Authors
  1. Siqi Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akihiro Yamamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siqi Peng .

Editor information

Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

  3. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  4. Dalhousie University, Halifax, NS, Canada

    Stan Matwin

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Peng, S., Yamamoto, A. (2020). Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61527-7_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61526-0

  • Online ISBN: 978-3-030-61527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation