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Discovering Co-Location Patterns and Rules in Uncertain Spatial Datasets Open Access


Other title
frequent pattern mining
co-location mining
uncertain spatial datasets
Type of item
Degree grantor
University of Alberta
Author or creator
Adilmagambetov, Aibek
Supervisor and department
Zaiane, Osmar (Computing Science)
Examining committee member and department
Zaiane, Osmar (Computing Science)
Yasui, Yutaka (Public Health Sciences)
Sander, Joerg (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Co-location mining, which focuses on the detection of co-location patterns, is one of the tasks of spatial data mining. A co-location pattern is a set of spatial features frequently located in close proximity of each other. Most previous works are based on transaction-free apriori-like algorithms which use user-defined thresholds and are designed for point objects. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. The approach we propose is based on a grid transactionization of geographic space and can be extended for spatial extended objects. Uncertainty of a feature presence in transactions is taken into account in our model. The statistical test is used instead of global thresholds to detect significant co-location patterns and rules. We evaluate our approach on real and synthetic data. In addition, we explain the data modeling framework which is used on a real dataset of pollutants and childhood cancer cases.
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