Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database Systems for Advanced Applications

DASFAA 2017: Database Systems for Advanced Applications pp 75–89Cite as

Discovering Interesting Co-location Patterns Interactively Using Ontologies

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10179))

Abstract

Co-location pattern mining, which discovers feature types that frequently appear in a nearby geographic region, plays an important role in spatial data mining. Common frameworks for mining co-location patterns generate numerous redundant patterns. Thus, several methods were proposed to overcome this drawback. However, most of these methods did not guarantee that the extracted co-location patterns were interesting for being generally based on statistical information. Thus, it is crucial to help the decision-maker choose interesting co-location patterns with an efficient interactive procedure. This paper proposed an interactive approach to discover interesting co-location patterns. First, ontologies were used to improve the integration of user knowledge. Second, an interactive process was designed to collaborate with the user to find interesting co-location patterns efficiently. Finally, a filter was designed to reduce the number of discovered co-location patterns in the result set further. The experimental results on both synthetic and real data sets demonstrated the effectiveness of our approach.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Huang, Y., Shekhar, S., Xiong, H.: Discovering co-location patterns from spatial data sets: a general approach. IEEE Trans. Knowl. Data Eng. (TKDE) 16(12), 1472–1485 (2004)

    Article  Google Scholar 

  2. Yoo, J.S., Bow, M.: Mining top-k closed co-location patterns. In: IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, pp. 100–105 (2011)

    Google Scholar 

  3. Wang, L., Zhou, L., Lu, J., et al.: An order-clique-based approach for mining maximal co-locations. Inf. Sci. 179(2009), 3370–3382 (2009)

    Article  MATH  Google Scholar 

  4. Xin, D., Shen, X., Mei, Q., et al.: Discovering interesting patterns through user’s interactive feedback. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 773–778 (2006)

    Google Scholar 

  5. Bao, X., Wang, L., Fang, Y.: OSCRM: a framework of ontology-based spatial co-location rule mining. J. Comput. Res. Dev. 52(Suppl.), 74–80 (2015)

    Google Scholar 

  6. Marinica, C., Guillet, F.: Knowledge-based interactive postmining of association rules using ontologies. IEEE Trans. Knowl. Data Eng. (TKDE) 22(6), 784–797 (2010)

    Article  Google Scholar 

  7. Yoo, J.S., Shekhar, S.: A partial join approach for mining co-location patterns. In: Annual ACM International Workshop on Geographic Information Systems, pp. 241–249 (2004)

    Google Scholar 

  8. Yoo, J.S., Shekhar, S., Celik, M.: A join-less approach for co-location pattern mining: a summary of results. In: IEEE International Conference on Data Mining, pp. 813–816 (2005)

    Google Scholar 

  9. Wang, L., Bao, Y., Lu, Z.: Efficient discovery of spatial co-location patterns using the iCPI-tree. Open Inf. Syst. J. 3(2), 69–80 (2009)

    Google Scholar 

  10. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  11. Guarino, N.: Formal ontology in information systems. In: International Conference Formal Ontology in Information Systems, pp. 3–15 (1998)

    Google Scholar 

  12. Maedche, A., Stabb, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)

    Article  Google Scholar 

  13. Bao, X., Wang, L., Chen, H.: Ontology-based interactive post-mining of interesting co-location patterns. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9932, pp. 406–409. Springer, Heidelberg (2016). doi:10.1007/978-3-319-45817-5_35

    Chapter  Google Scholar 

  14. Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)

    MATH  Google Scholar 

  15. Yu, H.: SVM selective sampling for ranking with application to data retrieval. In: ACM International Conference on Knowledge Discovery in Databases, pp. 354–363 (2005)

    Google Scholar 

  16. Shen, X., Zhai, C.: Active feedback in ad hoc information retrieval. In: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 59–66 (2005)

    Google Scholar 

  17. Horrocks, I., Patel-Schneider, P.F.: A proposal for an OWL rules language. In: International Conference World Wide Web, pp. 723–731 (2004)

    Google Scholar 

  18. Grosso, W.E., Eriksson, H., Fergerson, R.W., Gennari, J.H., Tu, S.W., Musen, M.A.: Knowledge modeling at the millennium. In: Workshop Knowledge Acquisition, Modeling and Management, pp. 16–21 (1999)

    Google Scholar 

  19. Storey, M.A., Noy, N.F., Musen, M., Best, C., Fergerson, R., Ernst, N.: Jambalaya: an interactive environment for exploring ontologies. In: International Conference Intelligent User Interfaces, p. 239 (2002)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by grants (No. 61472346, No.61262069, No. 61662086) from the National Natural Science Foundation of China, by grants (No. 2016FA026, No. 2015FB149, and No. 2015FB114) from the Science Foundation of Yunnan Province and by the Spectrum Sensing and borderlands Security Key Laboratory of Universities in Yunnan (C6165903).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Xuguang Bao & Lizhen Wang

Authors
  1. Xuguang Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lizhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lizhen Wang .

Editor information

Editors and Affiliations

  1. Royal Melbourne Institute of Technology , Melbourne, Australia

    Zhifeng Bao

  2. Northwestern University , Evanston, Illinois, USA

    Goce Trajcevski

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  4. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bao, X., Wang, L. (2017). Discovering Interesting Co-location Patterns Interactively Using Ontologies. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55705-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55704-5

  • Online ISBN: 978-3-319-55705-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation