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

  • Uncertain spatial datasets
  • Co-location mining
  • Frequent pattern mining

• Graduation date

  Fall 2012

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R38T5H

• 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.