Adnan, MuhammadRuddle, Roy A.Christian Tominski and Tatiana von Landesberger2018-06-022018-06-022018978-3-03868-064-2https://doi.org/10.2312/eurova.20181110https://diglib.eg.org:443/handle/10.2312/eurova20181110This paper explores how a set-based visual analytics approach could be useful for analyzing customers' shopping behavior, and makes three main contributions. First, it describes the scale and characteristics of a real-world retail dataset from a major supermarket. Second, it presents a scalable visual analytics workflow to quickly identify patterns in shopping behavior. To assess the workflow, we conducted a case study that used data from four convenience stores and provides several insights about customers' shopping behavior. Third, from our experience with analyzing real-world retail data and comments made by our industry partner, we outline four research challenges for visual analytics to tackle large set intersection problems.Humancentered computingVisual analyticsInformation systemsData mining10.2312/eurova.2018111037-41