Methods for mining co–location patterns with extended spatial objects

Bembenik, R. ; Jóźwicki, W. ; Protaziuk, G.

International Journal of Applied Mathematics and Computer Science, Tome 27 (2017) no. 4, pp. 681-695.

Résumé

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 EXCOM are compared with DEOSP, highlighting the advantages and downsides of both approaches.

Détail

Keywords: spatial data mining, colocation patterns, extended objects
Mots-clés : dane przestrzenne, eksploracja danych, obiekt rozszerzony

Bibliographie

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] Bembenik, R. and Rybiński, 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.

[7] Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996). From data mining to knowledge discovery in databases, AI Magazine 17(3): 37.

[8] 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.

[9] 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] 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.

[11] Li, D.,Wang, S. and Li, D. (2016). Spatial Data Mining: Theory and Application, Springer, Berlin/Heidelberg.

[12] 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.

[13] Lisi, F. A. and Malerba, D. (2004). Inducing multi-level association rules from multiple relations, Machine Learning 55(2): 175–210.

[14] 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.

[15] Okabe, A., Boots, B., Sugihara, K. and Chiu, S.N. (2009). Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, John Wiley Sons, Chichester.

[16] Oracle (2017). Oracle Spatial Developer’s Guide, https://docs.oracle.com/cd/B28359_01/appdev.111/b28400/sdo_objgeom.htm#SPATL120.

[17] PostGIS (2017). Buffer operation in PostGIS, http://www.postgis.net/docs/ST_Buffer.html.

[18] Rineau, L. (2017). 2D conforming triangulations and meshes. CGAL user and reference manual, https://doc.cgal.org/latest/Mesh_2/index.html.

[19] 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.

[20] Shekhar, S. and Xiong, H. (2007). Encyclopedia of GIS, Springer Science Business Media, New York, NY.

[21] 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.

[22] 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.

[23] 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.