iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11042-012-1007-2
A framework of spatial co-location pattern mining for ubiquitous GIS | Multimedia Tools and Applications Skip to main content

Advertisement

Springer Nature Link
Log in

A framework of spatial co-location pattern mining for ubiquitous GIS

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A spatial co-location pattern represents relationships between spatial features that are frequently located in close proximity to one another. Such a pattern is one of the most important concepts for geographic context awareness of ubiquitous Geographic Information System (GIS). We constructed a framework for co-location pattern mining using the transaction-based approach, which employs maximal cliques as a transaction-type dataset; we first define transaction-type data and verify that the definition satisfies the requirements, and we also propose an efficient way to generate all transaction-type data. The constructed framework can play a role as a theoretical methodology of co-location pattern mining, which supports geographic context awareness of ubiquitous GIS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Agarwal R, Srikant R (1994) Fast Algorithms for Mining Association Rules. In: Proceedings of 20th Conference on Very Large Databases, 1994

  2. Al-Naymat G (2008) Enumeration of maximal clique for mining spatial co-location patterns. In: Proceeding of IEEE/ACS International Conference on Computer Systems and Applications, 2008

  3. Beigl M (1999) Using spatial co-location for coordination in ubiquitous computing environments. In: Handheld and Ubiquitous Computing. First International Symposium, HUC'99

  4. Burian L, Marchetti M, Carmagnola F, Gena C, Torre I (2006) The Role of Ontologies in Context-aware Recommender Systems. In: Proceedings of 7th International Conference on Mobile Data Management, 2006

  5. Capilla R (2006) Context-aware Architectures for Building Service-Oriented SystemS. In: Proceeding of the Conference on Software Maintenance and Reengineering, IEEE, 2006, pp 300-303

  6. Cazals F, Karande C (2008) A note on the problem of reporting maximal cliques. Theor Comput Sci 407(1):564–568

    Article  MATH  MathSciNet  Google Scholar 

  7. Celtic M (2011) Discovering partial spatio-temporal co-occurrence patterns. In: Proceedings of 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), pp 116-120

  8. Chong MK, Kawsar F, Gellersen H (2011) Spatial co-location for device association: the connected object way. In: Proceedings of the 2011 international workshop on Networking and object memories for the internet of things

  9. Cover TM, Thomas JA (2006) Elements of Information Theory. Wiley InterScience

  10. Daniel GA (1987) Spatial Autocorrelation: A Primer. Association of American Geographers.

  11. Diggle PJ, Chetwynd AG (1983) Statistical Analysis of Point Process. Chapman and Hall.

  12. Geographic Information Systems as an Integrating Technology: http://www.colorado.edu/geography/gcraft/notes/intro/intro.html

  13. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM-SIGMOD International Conference on Management of Data, 2000

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

    Article  Google Scholar 

  15. Hunter A (2000) The Road to Ubiquitous Geographic Information Systems Roam Anywhere - Remain Connected. In: Proceedings of 12th Annual Colloquium of the Spatial Information Research Centre, 2000

  16. Jiang Y, Wang L, Lu Y, Chen H (2010) Discovering both positive and negative co-location rules. In: Proceedings of the 2nd International Conference on Software Engineering and Data Mining 2010, pp 398-403

  17. Kim SK, Kim Y, Kim U (2011) Maximal Cliques Generating Algorithm for Spatial Co-location Pattern Mining. In: Proceedings of 8th FIRA International Conference on Secure and Trust Computing Data Management and Applications 2011, vol 186, pp 241-250

  18. MACE (MAximal Clique Enumerater, ver. 2.2): http://research.nii.ac.jp/~uno/code/mace22.zip

  19. Mart’ınez-Ballesteros M, Troncoso A, Mart’ınez-A’lvarez F, Riquelme JC (2010) Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering 17(3):227–242

    Google Scholar 

  20. Morimoto Y (2001) Mining Frequent Neighboring Class Sets in Spatial Databases. In: Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining 2001, pp 353-358

  21. Qian F, Yin L, He Q, He J (2009) Mining spatio-temporal co-location patterns with weighted sliding window. In: Proceeding of IEEE International Conference on Intelligent Computing and Intelligent Systems 2009, vol 3, pp 181-185

  22. Rinzivillo S, Turini F (2005) Extracting spatial association rules from spatial transactions. In: Proceedings of the 13rd annual ACM international workshop on Geographic information systems 2005, pp 79 -86

  23. Shekhar S, Chawla S (2003) Spatial Databases: A Tour. Prentice Hall

  24. Spatial Data Transfer Standard: http://mcmcweb.er.usgs.gov/sdts/index.html

  25. Tan PN, Kumar V, Srivastava J (2002) Selecting the right interestingness measure for association patterns. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2002, pp 183

  26. Tan PN, Steinbach M, Kumar V (2006) Introduction to Data Mining, Addison-Wesley, pp 330

  27. Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Economic Geography, pp 234-240

  28. Tomita E, Tanaka A, Takahashi H (2006) The worst-case time complexity for generating all maximal cliques and computational experiments. Theor Comput Sci 363:28–42

    Article  MATH  MathSciNet  Google Scholar 

  29. Valley N, Lin Z, Lim S (2008) Fast Spatial Co-location Mining Without Cliqueness Checking. In: Proceedings of the 17th ACM Conference on Information and knowledge

  30. Verhein F, Al-Naymat G (2007) Fast Mining of Complex Spatial Co-location Patterns Using GLIMIT. In: Proceedings of the 7th IEEE International Conference on Data Mining Workshops 2007, pp 679-684

  31. Wan Y, Zhou C (2011) QuCOM: K nearest features neighborhood based qualitative spatial co-location patterns mining algorithm. 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), pp 54-59

  32. Weng C, Chen Y (2010) Fuzzy association rules from uncertain data. Knowledge and Information Systems 2010, vol 23(2), pp 129-152

    Google Scholar 

  33. Xiao X, Xie S, Luo A, Ma W (2008) Density Based Co-Location Pattern Discovery. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2008

  34. Xiong H, Shekhar S, Huang Y, Kumar V, Ma X, Yoo J (2004) A Framework for Discovering Co-Location Patterns in Data Sets with Extended Spatial Objects. In: Proceedings of 4th SIAM International Conference on Data Mining 2004

  35. Yin Y, Zhong Z, Wang Y (2008) Mining quantitative association rules by interval clustering. J Comput Inform Syst 4(2):609–616

    Google Scholar 

  36. Yoo JS, Bow M (2011) Mining top-k closed co-location patterns. In: Proceedings of 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), pp 100-105

  37. Yoo JS, Shekhar S (2004) A Partial Join Approach for Mining Colocation Patterns. In: Proceedings of ACM International Symposium Advances in Geographic Information Systems 2004

  38. Yoo JS, Shekhar S (2006) A Join-less Approach for Mining Spatial Colocation Patterns. IEEE Transactions on Knowledge and Data Engineering 2006, vol 18, pp 1323-1337

  39. ZEUS 10.1: http://ctn.cmworld.co.kr/xe/Z_info

Download references

Acknowledgments

This paper was supported by Faculty Research Fund, Sungkyunkwan University, 2011.

Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea

    Seung Kwan Kim, Jee Hyung Lee & Ungmo Kim

  2. Database /Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Republic of Korea

    Keun Ho Ryu

Authors
  1. Seung Kwan Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jee Hyung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keun Ho Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ungmo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ungmo Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, S.K., Lee, J.H., Ryu, K.H. et al. A framework of spatial co-location pattern mining for ubiquitous GIS. Multimed Tools Appl 71, 199–218 (2014). https://doi.org/10.1007/s11042-012-1007-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1007-2

Keywords

Search

Navigation