Abstract
Finding meaningful sets of co-purchased products allows retailers to manage inventory better and develop market strategies. Analyzing the baskets of products, known as market basket analysis, is typically carried out using association rule mining or community detection approach. This article uses both methods to investigate a transaction dataset collected from a brick-and-mortar grocery store. The findings reveal interesting purchasing patterns of local residents and prompt us to consider dynamic modeling of the product network in the future.
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