Skip to main content
SpringerLink
Log in

Top-k probabilistic prevalent co-location mining in spatially uncertain data sets

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions: 1) the concept of the top-k probabilistic prevalent co-locations based on a possible world model is defined; 2) a framework for discovering the top-k probabilistic prevalent co-locations is set up; 3) a matrix method is proposed to improve the computation of the prevalence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for exact solutions; 4) a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5) an approximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Huang Y, Shekhar S, Xiong H. Discovering co-location patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(12): 1472–1485

    Article  Google Scholar 

  2. Yoo J S, Shekhar S, Smith J, Kumquat J P. A partial join approach for mining co-location patterns. In: Proceedings of the 12th ACM International Workshop on Geographic Information Systems. 2004, 241–249

    Google Scholar 

  3. Yoo J S, Shekhar S. A joinless approach for mining spatial colocation patterns. IEEE Transactions on Knowledge and Data Engineering. 2006, 18(10): 1323–1337

    Article  Google Scholar 

  4. Wang L Z, Bao Y Z, Lu J, Yip J. A new join-less approach for colocation pattern mining, In: Proceedings of the 8th IEEE International Conference on Computer and Information Technology. 2008, 197–202

    Google Scholar 

  5. Wang L Z, Zhou L, Lu J, Yip J. An order-clique-based approach for mining Maximal co-locations. Information Sciences, 2009, 179(19): 3370–3382

    Article  MATH  Google Scholar 

  6. Xiao X Y, Xie X, Luo Q, Ma W Y. Density based co-location pattern discovery. In: Proceedings of the 16th ACM International Conference on Advances in Geographic Information Systems. 2008, 11–20

    Google Scholar 

  7. Celik M, Kang J M, Shekhar S. Zonal co-location pattern discovery with dynamic parameters. In: Proceedings of the 7th IEEE International Conference on Data Mining. 2007, 433–438

    Google Scholar 

  8. Mohan P, Shekhar S, Shine J A, Rogers J P, Jiang Z, Wayant N. A neighborhood graph based approach to regional co-location pattern discovery: a summary of results. In: Proceedings of the 19th ACM International Conference on Advances in Geographic Information Systems. 2011, 122–131

    Google Scholar 

  9. Sengstock C, Gertz M, Canh T V. Spatial interestingness measures for co-location pattern mining. In: Proceedings of the 12th IEEE International Conference on Data Mining. 2012, 821–826

    Google Scholar 

  10. Huang Y, Pei J, Xiong H. Mining co-location patterns with rare events from spatial data sets. GeoInformatica, 2006, 10(3): 239–260

    Article  Google Scholar 

  11. Feng L, Wang L Z, Gao S J. A new approach of mining co-location patterns in spatial datasets with rare feature. Journal of Nanjing University (Natural Sciences), 2012, 48(1): 99–107

    Google Scholar 

  12. Ouyang Z P, Wang L Z, Chen H M. Mining spatial co-location patterns for fuzzy objects. Jisuanji Xuebao (Chinese Journal of Computers), 2011, 34(10): 1947–1955

    Google Scholar 

  13. Liu Z, Huang Y. Mining co-locations under uncertainty. In: Proceedings of International Symposium on Spatial and Temporal Databases. 2013, 429–446

    Chapter  Google Scholar 

  14. Lu Y, Wang L, Zhang X. Mining frequent co-location patterns from uncertain data. Journal of Frontiers of Computer Science and Technology, 2009, 3(6): 656–664

    Google Scholar 

  15. Wang L Z, Wu P P, Chen H M. Finding probabilistic prevalent colocations in spatially uncertain data sets. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(4): 790–804

    Article  MathSciNet  Google Scholar 

  16. Yi K, Li F F, Kollios G, Srivastava D. Efficient processing of top-k queries in uncertain databases with x-relations. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(12): 1669–1682

    Article  Google Scholar 

  17. Beskales G, Soliman M A, Ilyas I F. Efficient search for the top-k probable nearest neighbors in uncertain databases. In: Proceedings of International Conference on Very Large Data Bases. 2008, 326–339

    Google Scholar 

  18. Hua M, Pei J, Zhang WJ, Lin X M. Ranking queries on uncertain data: a probabilistic threshold approach. In: Proceedings of the 2008 ACM International Conference on Management of Data. 2008, 673–686

    Google Scholar 

  19. Liu X J, Ye M, Xu J L, Tian Y, Lee W C. K-selection query over uncertain data. In: Proceedings of International Conference on Database Systems for Advanced Applications. 2010, 444–459

    Chapter  Google Scholar 

  20. Pietracaprina A, Riondato M, Upfla E, Vandin F. Mining top-k frequent itemsets through progressive sampling. Data Mining and Knowledge Discovery, 2010, 21(2): 310–326

    Article  MathSciNet  Google Scholar 

  21. Wu C W, Shie B E, Tseng V S, Yu P S. Mining top-k high utility itemsets. In: Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining. 2012, 78–86

    Google Scholar 

  22. Zhu F D, Qu Q, Lo D, Yan X F, Han J W, Yu P S. Mining top-klarge structural patterns in a massive network. Proceedings of the VLDB Endowment, 2011, 4(11): 807–818

    Google Scholar 

  23. Bartolini I, Ciaccia P, Patella M. Getting the best from uncertain data: the correlated case. In: Proceedings of the 20th Italian Symposium on Advanced Database Systems. 2012, 43–50

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Lizhen Wang, Jun Han, Hongmei Chen & Junli Lu

Authors
  1. Lizhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongmei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junli Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongmei Chen.

Additional information

Lizhen Wang received her MS in Computational Mathematics from Yunnan University, Kunming, China in 1988. She received her PhD in Computer Science from the University of Huddersfield, UK. She is now a professor and PhD supervisor in Yunnan University. Her main research interests include data mining, data warehouses, and computer algorithms.

Jun Han received her MS in Computer Science from Yunnan University, Kunming, China, in 2013. Her main research interests include spatial data warehouses and spatial data mining.

Hongmei Chen received her PhD in Computer Science from Yunnan University, Kunming, China, in 2012. She is a lecturer at Yunnan University. Her main research interests include data mining and data warehouses.

Junli Lu received her MS in Computer Science from Yunnan University, Kunming, China, in 2007, where she is currently a PhD candidate. She is an associate professor at Yunnan Minzu University, China. Her main research interests include spatial data mining and computer algorithms.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Han, J., Chen, H. et al. Top-k probabilistic prevalent co-location mining in spatially uncertain data sets. Front. Comput. Sci. 10, 488–503 (2016). https://doi.org/10.1007/s11704-015-4196-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-015-4196-9

Keywords

Search

Navigation