<![CDATA[This study aims to uncover consumer purchasing patterns in supermarkets to support more targeted sales strategies. The primary focus is on identifying products that are frequently bought together and their relationship with contextual factors such as payment methods, seasons, and discount status. The main challenge lies in handling transactional data that is highly diverse (high cardinality) and sparsely co-occurring, necessitating an approach capable of generating relevant association patterns. To address this, the study implements an integrated approach combining the ECLAT and FP-Growth algorithms in Market Basket Analysis. ECLAT is employed to filter items with low frequency through a TID-List structure, resulting in a more focused set of transactional data for FP-Growth processing. FP-Growth is then used to identify frequently co-occurring product and attribute combinations and to form association rules based on support, confidence, and lift values. The research data comprises 10,000 transactions with 13 attributes, focusing on Product, Payment_Method, Discount_Applied, Season, and City. As a result, ECLAT successfully filtered 81 products and 101 frequently occurring contextual attributes. FP-Growth subsequently discovered 407 itemset patterns, with 13 valid patterns forming association rules between products and contextual attributes. Additionally, three-item patterns were found for watch products associated with discounts and seasons. The contribution of this study lies in demonstrating that the integration of ECLAT and FP-Growth can serve as an effective method for discovering consumer shopping patterns based on transactional context, thereby supporting data-driven business decision-making.]]>
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