School of Information Management, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China.
Comput Intell Neurosci. 2022 Mar 15;2022:6439315. doi: 10.1155/2022/6439315. eCollection 2022.
In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.
在现代经济快速发展的背景下，信息在经济领域尤为重要，信息决定着企业的决策。因此，如何快速挖掘出有利于企业的信息成为了关键问题。本课题将数据挖掘技术应用于经济智能系统中，通过信息融合获取经济智能系统的数据对象模型。本文在此基础上分析了其对象之间的相互关系，并运用数据挖掘相关方法对其进行挖掘。经济智能系统的建立不仅涉及经济系统的数学模型的建立，还包括应用于这些模型的算法的研究。如何应用算法从数据库中的潜在信息中快速准确地提取所需的经济智能领域信息，或者提供一种寻找最佳解决方案的方法，涉及到关联规则和分类预测方法的使用。数据挖掘算法的应用可以用于研究经济智能系统的应用。本文开发并设计了一个经济智能信息数据库，并在此基础上实现了经济智能系统，实现了研究成果。最后，本文对数据集进行了测试，结果表明，该算法的分类准确率比 ID3 算法高 2.64%。
