Automated Retail Area Cluster Detection

The main objective of this project is to develop a retail cluster detection algorithm and improve the accuracy of the identification. The final deliverable will be an algorithm that runs through Google dataflow that will be able to ingest a month of users’ location breadcrumbs and output user-location clusters. The output of the algorithm will be a unique cluster identifier that will be used in the visit algorithm for visit identification. Besides latitude and longitude data, the project will have access to altitude information. This can be useful for determining clusters in multi-level locations (such as malls).This algorithm will help recognizing the customer visiting pattern observed based on user ID and allow accurate profiling for weather related content or advertisement targeting for enhanced user experience

Intern: 
Anxin Zhao
Faculty Supervisor: 
Nick Koudas
Province: 
Ontario
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