Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12–14, 2024, Ningbo, China

Research Article

Application of Data Mining Techniques in Enterprise Decision Support Systems

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  • @INPROCEEDINGS{10.4108/eai.12-1-2024.2347185,
        author={Dawei  Zhao and Nianchang  Yu},
        title={Application of Data Mining Techniques in Enterprise Decision Support Systems},
        proceedings={Proceedings of the 3rd International Conference on Big Data Economy and Digital Management, BDEDM 2024, January 12--14, 2024, Ningbo, China},
        publisher={EAI},
        proceedings_a={BDEDM},
        year={2024},
        month={6},
        keywords={data mining decision support systems effectiveness evaluation market segmentation product sales customer relationship management},
        doi={10.4108/eai.12-1-2024.2347185}
    }
    
  • Dawei Zhao
    Nianchang Yu
    Year: 2024
    Application of Data Mining Techniques in Enterprise Decision Support Systems
    BDEDM
    EAI
    DOI: 10.4108/eai.12-1-2024.2347185
Dawei Zhao1,*, Nianchang Yu2
  • 1: Lyceum of the Philippines University
  • 2: Powerchina Huadong Engineering Corporation Limited
*Contact email: dawei.zhao@lpunetwork.edu.ph

Abstract

This paper delves into the application of data mining techniques in enterprise decision support systems and methods for evaluating their effectiveness. The article first provides a comprehensive theoretical framework by introducing the fundamental theories and background knowledge of data mining. It then analyzes the specific applications of data mining technology in the realm of business decision-making through various practical cases, such as market segmentation, product sales trend analysis, and customer relationship management. These cases not only demonstrate the practical utility of data mining technology but also highlight its diversity of applications in different business scenarios. The latter part of the article focuses on the evaluation framework for the effectiveness of data mining applications, showcasing the strengths and weaknesses of various data mining techniques through comparative experiments and providing a set of evaluation criteria. The article aims to combine theory and practice, offering valuable references and guidance for enterprises in the selection and optimization of data mining solutions.