<![CDATA[In the rapidly evolving digital era, understanding customer purchasing behavior is crucial for marketing strategies and business development. This study uses the K-means clustering algorithm to analyze and segment customer purchasing behavior. This algorithm effectively partitions data into groups based on similar characteristics. The aim of this study is to identify purchasing behavior patterns using attributes such as purchase frequency, expenditure amount, and product types. By segmenting customers into homogeneous groups, companies can design more effective marketing strategies and better personalization. The results show that the K-means clustering method successfully segments customers based on similar behavior patterns, which can be used for market segmentation and strategy development. The application of this algorithm in purchasing behavior analysis is expected to provide deep insights and support better business decision-making, offering a competitive advantage for companies.]]>
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