Methods for mining co–location patterns with extended spatial objects

Bembenik, Robert; Jóźwicki, Wiktor; Protaziuk, Grzegorz

doi:10.1515/amcs-2017-0047

Abstract

The paper discusses various approaches to mining co-location patterns with extended spatial objects. We focus on the properties of transaction-free approaches EXCOM and DEOSP, and discuss the differences between the method using a buffer and that employing clustering and triangulation. These theoretical differences between the two methods are verified experimentally. In the performed tests three different implementations of EXCOMare compared with DEOSP, highlighting the advantages and downsides of both approaches.

References

Adilmagambetov, A., Zaiane, O.R. and Osornio-Vargas, A. (2013). Discovering co-location patterns in datasets with extended spatial objects, International Conference on Data Warehousing and Knowledge Discovery, Berlin, Germany, pp. 84-96.
Search in Google Scholar Back to article
Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules, 20th International Conference Very Large Data Bases, VLDB, Santiago de Chile, Chile, pp. 487-499.
Search in Google Scholar Back to article
Appice, A., Berardi, M., Ceci, M. and Malerba, D. (2005). Mining and filtering multi-level spatial association rules with ares, International Symposium on Methodologies for Intelligent Systems, Saratoga Springs, NY, USA, pp. 342-353.
Search in Google Scholar Back to article
Barua, S. and Sander, J. (2011). SSCP: Mining statistically significant co-location patterns, International Symposium on Spatial and Temporal Databases, Minneapolis, MN, USA, pp. 2-20.
Search in Google Scholar Back to article
Bembenik, R., Ruszczyk, A. and Protaziuk, G. (2014). Discovering collocation rules and spatial association rules in spatial data with extended objects using Delaunay diagrams, International Conference on Rough Sets and Intelligent Systems Paradigms, Granada/Madrid, Spain, pp. 293-300.
Search in Google Scholar Back to article
Bembenik, R. and Rybi´nski, H. (2009). FARICS: A method of mining spatial association rules and collocations using clustering and Delaunay diagrams, Journal of Intelligent Information Systems 33(1): 41-64.10.1007/s10844-008-0076-1
Open DOI Search in Google Scholar Back to article
Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996). From data mining to knowledge discovery in databases, AI Magazine 17(3): 37.
Search in Google Scholar Back to article
Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V. and Mascolo, C. (2013). Geo-spotting: Mining online location-based services for optimal retail store placement, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, pp. 793-801.
Search in Google Scholar Back to article
Kim, S.K., Lee, J.H., Ryu, K.H. and Kim, U. (2014). A framework of spatial co-location pattern mining for ubiquitous GIS, Multimedia Tools and Applications 71(1): 199-218.10.1007/s11042-012-1007-2
Search in Google Scholar Back to article
Koperski, K. and Han, J. (1995). Discovery of spatial association rules in geographic information databases, in M.J. Egenhofer and J.R. Herring (Eds.), Advances in Spatial Databases, Springer, Berlin/Heidelberg, pp. 47-66.10.1007/3-540-60159-7_4
Search in Google Scholar Back to article
Li, D.,Wang, S. and Li, D. (2016). Spatial Data Mining: Theory and Application, Springer, Berlin/Heidelberg.10.1007/978-3-662-48538-5_8
Search in Google Scholar Back to article
Li, J., Zaïane, O.R. and Osornio-Vargas, A. (2014). Discovering statistically significant co-location rules in datasets with extended spatial objects, International Conference on Data Warehousing and Knowledge Discovery, Munich, Germany, pp. 124-135.
Search in Google Scholar Back to article
Lisi, F. A. and Malerba, D. (2004). Inducing multi-level association rules from multiple relations, Machine Learning 55(2): 175-210.10.1023/B:MACH.0000023151.65011.a3
Open DOI Search in Google Scholar Back to article
Loglisci, C., Ceci, M. and Malerba, D. (2010). Relational learning of disjunctive patterns in spatial networks, 1st Workshop on Dynamic Networks and Knowledge Discovery, Barcelona, Spain, pp. 17-28.
Search in Google Scholar Back to article
Okabe, A., Boots, B., Sugihara, K. and Chiu, S.N. (2009). Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, John Wiley & Sons, Chichester. Oracle (2017). Oracle Spatial Developer’s Guide, https://docs.oracle.com/cd/B28359_01/appdev.111/b28400/sdo_objgeom.htm#SPATL120.
Search in Google Scholar Back to article
PostGIS (2017). Buffer operation in PostGIS, http://www.postgis.net/docs/ST_Buffer.html.
Search in Google Scholar Back to article
Rineau, L. (2017). 2D conforming triangulations and meshes. CGAL user and reference manual, https://doc.cgal.org/latest/Mesh_2/index.html.
Search in Google Scholar Back to article
Shekhar, S. and Huang, Y. (2001). Discovering spatial co-location patterns: A summary of results, International Symposium on Spatial and Temporal Databases, Redondo Beach, CA, USA, pp. 236-256.
Search in Google Scholar Back to article
Shekhar, S. and Xiong, H. (2007). Encyclopedia of GIS, Springer Science & Business Media, New York, NY.10.1007/978-0-387-35973-1_368
Search in Google Scholar Back to article
Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X. and Yoo, J. S. (2004). A framework for discovering co-location patterns in data sets with extended spatial objects, 4th SIAM International Conference on Data Mining, Lake Buena Vista, FL, USA, pp. 78-89.
Search in Google Scholar Back to article
Yang, X. and Cui, W. (2008). A novel spatial clustering algorithm based on Delaunay triangulation, International Conference on Earth Observation Data Processing and Analysis, Wuhan, China, pp. 728530-728530.
Search in Google Scholar Back to article
Zheng, Y., Capra, L., Wolfson, O. and Yang, H. (2014). Urban computing: Concepts, methodologies, and applications, ACM Transactions on Intelligent Systems and Technology 5(3): 38.10.1145/2629592
Search in Google Scholar Back to article