In a general manner, customer relationship management engages in understanding customer needs and meet them. Most of the investments are either far from customer needs or based on a primitive data collection method. However, customers mainly do not behave with the same ideas to shop in the retail domain. Several studies aim to understand the visiting purposes of customers using various methods. This study seeks to uncover the visit purposes of customers from their paths. Due to customers' unpredictable moods and plenty of stores in the shopping mall, the discovered paths are usually too complicated to analyze. Process mining that can overcome this obstacle is a method that creates process flows from event logs in the databases. In this study, the visited stores were seen as an activity in a business process. PALIA, a discovery algorithm in process mining, was applied to find and cluster customer paths. This study contributes to the literature by examining customer needs from their indoor paths, which were created by the PALIA algorithm. It facilitates to analyze discrepancies among the visits for the same customer. Moreover, the discovered paths are considered according to the age groups predicted by Levenshtein fuzzy kNN (L-FkNN).