Abstract
Spatial co-location pattern mining discovers the subsets of features whose events are frequently located together in geographic space. The current research on this topic adopts a threshold-based approach that requires users to specify in advance the thresholds of distance and prevalence. However, in practice, it is not easy to specify suitable thresholds. In this article, we propose a novel iterative mining framework that discovers spatial co-location patterns without predefined thresholds. With the absolute and relative prevalence of spatial co-locations, our method allows users to iteratively select informative edges to construct the neighborhood relationship graph until every significant co-location has enough confidence and eventually to discover all spatial co-location patterns. The experimental results on real world data sets indicate that our framework is effective for prevalent co-locations discovery.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arge L, Procopiuc O, Ramaswamy S, Suel T, Vitter JS (1998) Scalable sweeping-based spatial join. In: Proceedings of the 24th international conference on very large data bases, New York, August 24–27, pp 570–581
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Bureau of Transportation Statistics. http://www.bts.gov/, 2010
Celik M, Shekhar S, Rogers JP, Shine JA (2006) Sustained emerging spatio-temporal co-occurrence pattern mining: a summary of results. In: Proceedings of the 18th IEEE international conference on tools with artificial intelligence, Washington, USA, November 13–15, pp 106–115
Celik M, Shekhar S, Rogers JP, Shine JA, Yoo JS (2006) Mixed-drove spatio-temporal co-occurrence pattern mining: a summary of results. In: Proceedings of the 6th international conference on data mining, Hong Kong, China, December 18–22, pp 119–128
Digital Chart of the World. http://www.maproom.psu.edu/dcw/, 2010
Huang Y, Pei J, Xiong H (2006) Mining co-Location patterns with rare events from spatial data sets. GeoInformatica 10(3): 239–260
Huang Y, Shekhar S, Xiong H (2004) Discovering colocation patterns from spatial datasets: a general approach. IEEE Trans Knowl Data Eng 16(12): 1472–1485
Huang Y, Zhang P, Zhang C (2008) On the relationships between clustering and spatial co-location pattern mining. Int J Artif Intell Tools 17(1): 55–70
Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, California, USA, August 12–15, pp 420–429
Lin Z, Lim SJ (2009) Optimal candidate generation in spatial co-location mining. In: Proceedings of the 2009 ACM symposium on applied computing, Hawaii, USA, March 9–12, pp 1441–1445
Munro R, Chawla S, Sun P (2003) Complex spatial relationships. In: Proceedings of the 3rd IEEE international conference on data mining, Melbourne, USA, December 19–22, pp 227–234
Nemhauser GL, Wolsey LA, Fisher ML (1978) An analysis of approximations for maximizing submodular set functions. Math Program 14(1): 265–294
Qian F, He Q, He J (2009) Mining spread patterns of spatio-temporal co-occurrences over zones. In: Proceedings of the international conference on computational science and its applications, Seoul, Korea, June 29–July 2, pp 686–701
Qian F, Yin L, He Q, He J (2009) Mining spatio-temporal co-location patterns with weighted sliding window. In: Proceedings of IEEE international conference on intelligent computing and intelligent systems, Shanghai, China, November 20–22, pp 181–185
Salam A, Khayal M (2011) Mining top-k frequent patterns without minimum support threshold. Knowl Inf Syst, pp 1–30
Salmenkivi M (2004) Evaluating attraction in spatial point patterns with an application in the field of cultural history. In: Proceedings of the 4th IEEE international conference on data mining, Brighton, UK, November 1–4, pp 511–514
Shekhar S, Huang Y (2001) Discovering spatial co-location patterns: A summary of results. In: Proceedings of the 7th international symposium on spatial and temporal databases, Redondo Beach, USA, July 12–15, pp 236–256
Sheng C, Hsu W, Lee ML, Tung AKH (2008) Discovering spatial interaction patterns. In: Proceedings of the 13th international conference on database systems for advanced applications, New Delhi, India, March 19–21, pp 95–109
Tobler W (1979) Cellular geography. Philosophy in geography, pages 379–386
Tzvetkov P, Yan X, Han J (2005) TSP: mining top-k closed sequential patterns. Knowl Inf Syst 7(4): 438–457
Wang L, Zhou L, Lu J, Yip J (2009) An order-clique-based approach for mining maximal co-locations. Inf Sci 179(19): 3370–3382
Xiao X, Xie X, Luo Q, Ma WY (2008) Density based co-location pattern discovery. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, Irvine, USA, November 5–7, pp 1–10
Xiong H, Shekhar S, Huang Y, Kumar V, Ma X, Yoo JS (2004) A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proceedings of the 4th SIAM international conference on data mining, Lake Buena, USA, April 22–24, vol 89, p 78
Yang B, Huang H (2010) TOPSIL-miner: an efficient algorithm for mining top-k significant itemsets over data streams. Knowl Inf Syst 23(2): 225–242
Yoo JS, Shekhar S (2006) A joinless approach for mining spatial colocation patterns. IEEE Trans Knowl Data Eng 18(10): 1323–1337
Yoo JS, Shekhar S, Kim S, Celik M (2006) Discovery of co-evolving spatial event sets. In: Proceedings of the 6th SIAM international conference on data mining, Bethesda, USA, November 20–22, pp 306–315
Yoo JS, Bow M (2009) Finding N-most prevalent colocated event sets. In: Proceedings of the 11th international conference on data warehousing and knowledge discovery, Linz, USA, August 31–September 2, pp 415–427
Zhang X, Mamoulis N, Cheung DW, Shou Y (2004) Fast mining of spatial collocations. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, USA, August 22–25, pp 384–393
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Qian, F., He, Q., Chiew, K. et al. Spatial co-location pattern discovery without thresholds. Knowl Inf Syst 33, 419–445 (2012). https://doi.org/10.1007/s10115-012-0506-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-012-0506-9