Discovering Co-Location Patterns and Rules in Uncertain Spatial Datasets

  • Author / Creator
    Adilmagambetov, Aibek
  • 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.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Zaiane, Osmar (Computing Science)
  • Examining committee members and their departments
    • Sander, Joerg (Computing Science)
    • Zaiane, Osmar (Computing Science)
    • Yasui, Yutaka (Public Health Sciences)