Data mining in economics: Unraveling merchant transactions for strategic insights

Abstract

This paper explores the application of data analysis and mining techniques in the domain of economics, with a specific focus on understanding merchant transaction characteristics. The study delves into fundamental theories, including classification tasks, regression missions, and relevance analysis, showcasing the versatility of these techniques in addressing economic challenges. Neural network models, such as Multilayer Perceptron and Auto Encoder, are introduced for handling complex economic data. The research emphasizes the importance of data mining in extracting valuable insights from real-world merchant transaction data, leading to the creation of the Merchant Transaction Feature Standard Database. A detailed data preprocessing method tailored to merchant transaction data is presented, addressing issues such as missing data, noise reduction, data integration, and transformation. The unique characteristics of real merchant transaction data, including sensitivity, concentration, sparsity, and the lack of label diversity, are outlined. The study concludes by highlighting the potential benefits of employing data mining techniques in optimizing marketing, merchant management, and risk management strategies. Overall, this paper contributes to advancing the understanding and practical applications of data analysis and mining techniques in economics.