Abstract
Co-location rule mining is one of the tasks of spatial data mining, which focuses on the detection of sets of spatial features that show spatial associations. Most previous methods are generally based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location rule mining problem. To solve these difficulties, a transactionization approach was recently proposed; designed to mine datasets with extended spatial objects. A statistical test is used instead of global thresholds to detect significant co-location rules. One major shortcoming of this work is that it limits the size of antecedent of co-location rules up to three features, therefore, the algorithm is difficult to scale up. In this paper we introduce a new algorithm that fully exploits the property of statistical significance to detect more general co-location rules. We use our algorithm on real datasets with the National Pollutant Release Inventory (NPRI). A classifier is also proposed to help evaluate the discovered co-location rules.
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AgroClimatic Information Service (ACIS). Live alberta weather station data, http://www.agric.gov.ab.ca/app116/stationview.jsp
Adilmagambetov, A., Zaiane, O.R., Osornio-Vargas, A.: Discovering co-location patterns in datasets with extended spatial objects. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 84–96. Springer, Heidelberg (2013)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)
Barua, S., Sander, J.: SSCP: Mining statistically significant co-location patterns. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 2–20. Springer, Heidelberg (2011)
Environment Canada. National Pollutant Release Inventory. Tracking Pollution in Canada, http://www.ec.gc.ca/inrp-npri/
Environment Canada. National Climate Data and Information. Canadian climate normals or averages 1971-2000, http://climate.weatheroffice.gc.ca/climate_normals/index_e.html
ESRI. ArcGIS Desktop: Release 10 (2011)
Hämäläinen, W., Nykanen, M.: Efficient discovery of statistically significant association rules. In: ICDM, pp. 203–212 (2008)
Hämäläinen, W.: Efficient discovery of the top-k optimal dependency rules with fisher’s exact test of significance. In: ICDM, pp. 196–205 (2010)
Hämäläinen, W.: Statapriori: an efficient algorithm for searching statistically significant association rules. KAIS 23(3), 373–399 (2010)
Hämäläinen, W.: Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. KAIS 32(2), 383–414 (2012)
Huang, Y., Pei, J., Xiong, H.: Mining co-location patterns with rare events from spatial data sets. Geoinformatica 10(3), 239–260 (2006)
Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: A summary of results. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 236–256. Springer, Heidelberg (2001)
Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S.: A framework for discovering co-location patterns in data sets with extended spatial objects. In: SDM (2004)
Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial co-location patterns. TKDE 18(10), 1323–1337 (2006)
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Li, J., Zaïane, O.R., Osornio-Vargas, A. (2014). Discovering Statistically Significant Co-location Rules in Datasets with Extended Spatial Objects. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_12
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DOI: https://doi.org/10.1007/978-3-319-10160-6_12
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