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Dynamic Determining of Time-window Parameters in Spatiotemporal Association Rules Mining
This project will address the problem of time-window parameter adaptation in the process of sequence association rules mining. During the proposed internship in Canada, the main task will be to develop the method and algorithm for implementing time-window adaptive parameters. The advantage of the algorithm is that the identification of events does not rely on the fixed-size time windows. It can reflect the dynamic evolution characteristics of different geographical phenomena. This research can be applied to other fields and allows for publication of papers on research results.
Faculty Supervisor:
Songnian Li
Student:
Partner:
Beijing University of Civil Engineering and Architecture
Discipline:
Engineering
Sector:
Technology; Sustainability & the Environment; Public Service, Policy, and Governance
University:
Toronto Metropolitan University
Program:
Globalink Research Award
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