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The integration of Artificial Intelligence (AI) technology has exerted a significant impact on the retail sector. Nevertheless, the adoption of AI carries substantial responsibilities and risks that fall squarely on the shoulders of senior managers. In this paper, the retail online dataset has been utilized. The contribution of the paper is an improved K-Means algorithm which has been customized for the segregation of product quantities by the top 5 customers. In addition to that, agglomerative clustering has been applied for the frequency of products which has been followed by the implementation of a Clustering algorithm to quantify individual customer purchases. The proposed DBSCAN algorithm categorizes products with similar costs by effectively grouping them. The outcome of the simulation results in the DBSCAN algorithm which exhibits improved performance in in terms of a substantial volume of data by sharing similar properties.

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Assessing Customer Retail Data Through the Application of Various Clustering Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Subhranil Das,
  • Rashmi Kumari,
  • Raghwendra Kishore Singh

摘要

The integration of Artificial Intelligence (AI) technology has exerted a significant impact on the retail sector. Nevertheless, the adoption of AI carries substantial responsibilities and risks that fall squarely on the shoulders of senior managers. In this paper, the retail online dataset has been utilized. The contribution of the paper is an improved K-Means algorithm which has been customized for the segregation of product quantities by the top 5 customers. In addition to that, agglomerative clustering has been applied for the frequency of products which has been followed by the implementation of a Clustering algorithm to quantify individual customer purchases. The proposed DBSCAN algorithm categorizes products with similar costs by effectively grouping them. The outcome of the simulation results in the DBSCAN algorithm which exhibits improved performance in in terms of a substantial volume of data by sharing similar properties.