Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

An Image Processing based Algorithm for Discovering Co-Location Patterns

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Shahbaz Ahmad, Muhammad Asif
10.5120/ijca2016912338

Shahbaz Ahmad and Muhammad Asif. An Image Processing based Algorithm for Discovering Co-Location Patterns. International Journal of Computer Applications 156(1):1-6, December 2016. BibTeX

@article{10.5120/ijca2016912338,
	author = {Shahbaz Ahmad and Muhammad Asif},
	title = {An Image Processing based Algorithm for Discovering Co-Location Patterns},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {1},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume156/number1/26670-2016912338},
	doi = {10.5120/ijca2016912338},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Spatial co-location patterns represents the subset of Boolean spatial features (e.g. Frontage roads, freeways) whose instances are often located in close geographic proximity. For instance, stagnant water founts and west Nile ailments are often co-located. The co-location pattern can be defined as an undirected connected graph in which every node represents a feature and every single edge denotes relationship (neighbourhood) between connecting features. Literature provides different approaches (including transaction based, join and join-less approaches) to discover co-location patterns. This paper proposes, implements and tests an image processing based algorithm to discover these patterns. The algorithm inputs minimum confidence measure (for statistical significance), neighbourhood distance threshold and set of Boolean spatial features, whose instances are represented as an image. It converts the image into binary image and then uses the concept of neighbourhood relationship (materialized using distance threshold) and confidence measure to mine the patterns. Furthermore, this paper provides implementation and testing of proposed algorithm in terms of time and space complexity.

References

  1. Zhang, X., et al. Fast mining of spatial collocations. in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004. ACM.
  2. Zala, M., et al., A Survey on Spatial Co-location Patterns Discovery from Spatial Datasets. arXiv preprint arXiv:1402.1327, 2014.
  3. Shekhar, S. and Y. Huang, Discovering spatial co-location patterns: A summary of results, in Advances in Spatial and Temporal Databases. 2001, Springer. p. 236-256.
  4. Adilmagambetov, A., O.R. Zaiane, and A. Osornio-Vargas, Discovering Co-location Patterns in Datasets with Extended Spatial Objects, in Data Warehousing and Knowledge Discovery. 2013, Springer. p. 84-96.
  5. Mohan, P., et al. A spatial neighborhood graph approach to regional co-location pattern discovery: A summary of results. in 19 th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, ACM-GIS. 2011.
  6. Koperski, K. and J. Han. Discovery of spatial association rules in geographic information databases. in Advances in spatial databases. 1995. Springer.
  7. Huang, Y., et al. Mining confident co-location rules without a support threshold. in Proceedings of the 2003 ACM symposium on Applied computing. 2003. ACM.
  8. Munro, R., S. Chawla, and P. Sun. Complex spatial relationships. in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. 2003. IEEE.
  9. Xiong, H., et al. A Framework for Discovering Co-Location Patterns in Data Sets with Extended Spatial Objects. in SDM. 2004. SIAM.
  10. Wang, J., W. Hsu, and M.L. Lee. A framework for mining topological patterns in spatio-temporal databases. in Proceedings of the 14th ACM international conference on Information and knowledge management. 2005. ACM.
  11. Shekhar, S., et al., Trends in spatial data mining. Data mining: Next generation challenges and future directions, 2003: p. 357-380.
  12. Barua, S. and J. Sander. Mining statistically sound co-location patterns at multiple distances. in Proceedings of the 26th International Conference on Scientific and Statistical Database Management. 2014. ACM.
  13. Shekhar, S., et al., Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2011. 1(3): p. 193-214.
  14. Yoo, J.S., S. Shekhar, and M. Celik. A join-less approach for co-location pattern mining: A summary of results. in Data Mining, Fifth IEEE International Conference on. 2005. IEEE.
  15. Shekhar, S. and Y. Huang. Co-location rules mining: A summary of results. in Proc. Spatio-temporal Symposium on Databases. 2001.

Keywords

Association rule mining, Co-Location Pattern discovery, Collocation Pattern, Image Processing, Spatial Association Pattern, Spatial Data mining