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Erschienen in: International Journal of Data Science and Analytics 2-3/2018

09.11.2017 | Regular Paper

Discovering co-location patterns with aggregated spatial transactions and dependency rules

verfasst von: Mohomed Shazan Mohomed Jabbar, Colin Bellinger, Osmar R. Zaïane, Alvaro Osornio-Vargas

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 2-3/2018

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Abstract

Co-location pattern mining focuses on finding associations among spatial features. Existing co-location pattern mining techniques mainly rely on frequency based thresholds which discard the rare patterns and find the noisy patterns. This could be avoided by evaluating co-location patterns based on their statistical significance. Recent studies focused on association rule mining have successfully adopted statistical tests to find significant rules. By transforming spatial data to transaction data, the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly. A transactionization mechanism has been recently proposed to achieve this. However, this method ignores the effect of general instances, with non-overlapping buffer regions, on the reference instances in their proximity. Addressing this, we propose a novel approach, AGT-Fisher, to robustly transform spatial data to transaction data and use statistically significant dependency rule searching methods to find co-location rules from them. Our work is motivated by an application in environmental health to investigate potential associations between air pollution and adverse birth outcomes in Canada. We used AGT-Fisher to find such associations from real datasets. The discovered co-location patterns were evaluated based on their statistical dependency and the empirical evidence, and results showed that our approach is more robust. Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights.

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Literatur
1.
Zurück zum Zitat Mohomed Jabbar, M.S., Zaïane, O.R., Osornio-Vargas, A.: Discovering spatial contrast and common sets with statistically significant co-location patterns. In: ACM Symposium on Applied Computing, ACM (2017) Mohomed Jabbar, M.S., Zaïane, O.R., Osornio-Vargas, A.: Discovering spatial contrast and common sets with statistically significant co-location patterns. In: ACM Symposium on Applied Computing, ACM (2017)
2.
Zurück zum Zitat Mohomed Jabbar, M.S., Zaïane, O.R.: Learning statistically significant contrast sets. In: 29th Canadian Conference on Artificial Intelligence, pp. 237–242. Springer (2016) Mohomed Jabbar, M.S., Zaïane, O.R.: Learning statistically significant contrast sets. In: 29th Canadian Conference on Artificial Intelligence, pp. 237–242. Springer (2016)
3.
Zurück zum Zitat Adilmagambetov, A., Zaiane, O.R., Osornio-Vargas, A.: Discovering co-location patterns in datasets with extended spatial objects. In: International Conference on Data Warehousing and Knowledge Discovery, pp. 84–96. Springer (2013) Adilmagambetov, A., Zaiane, O.R., Osornio-Vargas, A.: Discovering co-location patterns in datasets with extended spatial objects. In: International Conference on Data Warehousing and Knowledge Discovery, pp. 84–96. Springer (2013)
4.
Zurück zum Zitat Brauer, M., Lencar, C., Tamburic, L., Koehoorn, M., Demers, P., Karr, C.: A cohort study of traffic-related air pollution impacts on birth outcomes. Environ. Health Perspect. 116(5), 680 (2008)CrossRef Brauer, M., Lencar, C., Tamburic, L., Koehoorn, M., Demers, P., Karr, C.: A cohort study of traffic-related air pollution impacts on birth outcomes. Environ. Health Perspect. 116(5), 680 (2008)CrossRef
5.
Zurück zum Zitat Bay, S.D., Pazzani, M.J.: Detecting group differences: mining contrast sets. Data Min. Knowl. Discov. 5(3), 213–246 (2001)CrossRefMATH Bay, S.D., Pazzani, M.J.: Detecting group differences: mining contrast sets. Data Min. Knowl. Discov. 5(3), 213–246 (2001)CrossRefMATH
6.
Zurück zum Zitat Antonie, L., Zaïane, O.R., Holte, R.C.: Redundancy reduction: does it help associative classifiers? In: ACM Symposium on Applied Computing, pp. 867–874. ACM (2016) Antonie, L., Zaïane, O.R., Holte, R.C.: Redundancy reduction: does it help associative classifiers? In: ACM Symposium on Applied Computing, pp. 867–874. ACM (2016)
8.
Zurück zum Zitat Hämäläinen, W.: Statapriori: an efficient algorithm for searching statistically significant association rules. Knowl. Inf. Syst. 23(3), 373–399 (2010)CrossRef Hämäläinen, W.: Statapriori: an efficient algorithm for searching statistically significant association rules. Knowl. Inf. Syst. 23(3), 373–399 (2010)CrossRef
9.
Zurück zum Zitat Hämäläinen, W.: Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. Knowl. Inf. Syst. 32(2), 383–414 (2012)CrossRef Hämäläinen, W.: Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. Knowl. Inf. Syst. 32(2), 383–414 (2012)CrossRef
10.
Zurück zum Zitat Lavigne, E., Yasseen, A.S., Stieb, D.M., Hystad, P., van Donkelaar, A., Martin, R.V., Brook, J.R., Crouse, D.L., Burnett, R.T., Chen, H., et al.: Ambient air pollution and adverse birth outcomes: differences by maternal comorbidities. Environ. Res. 148, 457–466 (2016)CrossRef Lavigne, E., Yasseen, A.S., Stieb, D.M., Hystad, P., van Donkelaar, A., Martin, R.V., Brook, J.R., Crouse, D.L., Burnett, R.T., Chen, H., et al.: Ambient air pollution and adverse birth outcomes: differences by maternal comorbidities. Environ. Res. 148, 457–466 (2016)CrossRef
11.
Zurück zum Zitat Li, J., Adilmagambetov, A., Mohomed Jabbar, M.S., Zaïane, O.R., Osornio-Vargas, A., Wine, O.: On discovering co-location patterns in datasets: a case study of pollutants and child cancers. GeoInformatica 20, 1–42 (2016)CrossRef Li, J., Adilmagambetov, A., Mohomed Jabbar, M.S., Zaïane, O.R., Osornio-Vargas, A., Wine, O.: On discovering co-location patterns in datasets: a case study of pollutants and child cancers. GeoInformatica 20, 1–42 (2016)CrossRef
12.
Zurück zum Zitat Li, J., Zaïane, O. R., Osornio-Vargas, A.: Discovering statistically significant co-location rules in datasets with extended spatial objects. In: Data Warehousing and Knowledge Discovery, pp. 124–135. Springer (2014) Li, J., Zaïane, O. R., Osornio-Vargas, A.: Discovering statistically significant co-location rules in datasets with extended spatial objects. In: Data Warehousing and Knowledge Discovery, pp. 124–135. Springer (2014)
13.
Zurück zum Zitat Ha, S., Hu, H., Roussos-Ross, D., Haidong, K., Roth, J., Xu, X.: The effects of air pollution on adverse birth outcomes. Environ. Res. 134, 198–204 (2014)CrossRef Ha, S., Hu, H., Roussos-Ross, D., Haidong, K., Roth, J., Xu, X.: The effects of air pollution on adverse birth outcomes. Environ. Res. 134, 198–204 (2014)CrossRef
15.
Zurück zum Zitat Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006)CrossRef Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006)CrossRef
16.
Zurück zum Zitat Webb, G.I.: Discovering significant rules. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 434–443 (2006) Webb, G.I.: Discovering significant rules. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 434–443 (2006)
17.
Zurück zum Zitat Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.: A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proceedings of the 2004 SIAM International Conference on Data Mining (SDM), (2004) Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.: A framework for discovering co-location patterns in data sets with extended spatial objects. In: Proceedings of the 2004 SIAM International Conference on Data Mining (SDM), (2004)
18.
Zurück zum Zitat Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: A summary of results. In :Proceedings of the 7th International Symposium on Spatial and Temporal Databases (SSTD), pp. 236–256 (2001) Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: A summary of results. In :Proceedings of the 7th International Symposium on Spatial and Temporal Databases (SSTD), pp. 236–256 (2001)
19.
Zurück zum Zitat Barua, S., Sander, J.: Sscp: mining statistically significant co-location patterns. In: Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD), pp. 2–20 (2011) Barua, S., Sander, J.: Sscp: mining statistically significant co-location patterns. In: Proceedings of the 12th International Symposium on Spatial and Temporal Databases (SSTD), pp. 2–20 (2011)
20.
Zurück zum Zitat Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)CrossRef Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)CrossRef
21.
Zurück zum Zitat Eick, C.F., Ding, R. Parmar W., Stepinski, T.F., Nicot, J.: Finding regional co-location patterns for sets of continuous variables in spatial datasets. In: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (2008) Eick, C.F., Ding, R. Parmar W., Stepinski, T.F., Nicot, J.: Finding regional co-location patterns for sets of continuous variables in spatial datasets. In: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (2008)
22.
24.
Zurück zum Zitat Jalali-Heravi, M., Zaïane, O.R.: A study on interestingness measures for associative classifiers In :Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1039–1046 (2010) Jalali-Heravi, M., Zaïane, O.R.: A study on interestingness measures for associative classifiers In :Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1039–1046 (2010)
25.
Zurück zum Zitat Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases In :Proceedings of International Symposium on Advances in Spatial Databases, pp. 47–66 (1995) Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases In :Proceedings of International Symposium on Advances in Spatial Databases, pp. 47–66 (1995)
26.
Zurück zum Zitat Cressie, N.: Statistics for Spatial Data (2015) Cressie, N.: Statistics for Spatial Data (2015)
27.
Zurück zum Zitat Hämäläinen, W.: New upper bounds for tight and fast approximation of fishers exact test in dependency rule mining. Comput. Stat. Data Anal. 93, 469–482 (2012)MathSciNetCrossRef Hämäläinen, W.: New upper bounds for tight and fast approximation of fishers exact test in dependency rule mining. Comput. Stat. Data Anal. 93, 469–482 (2012)MathSciNetCrossRef
28.
Zurück zum Zitat Chou, Y. H.: Exploring Spatial Analysis in Geographic Information Systems (1997) Chou, Y. H.: Exploring Spatial Analysis in Geographic Information Systems (1997)
Metadaten
Titel
Discovering co-location patterns with aggregated spatial transactions and dependency rules
verfasst von
Mohomed Shazan Mohomed Jabbar
Colin Bellinger
Osmar R. Zaïane
Alvaro Osornio-Vargas
Publikationsdatum
09.11.2017
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 2-3/2018
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-017-0079-5

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