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Human Resource Allocation Based on Fuzzy Data Mining Algorithm

机译:基于模糊数据挖掘算法的人力资源配置

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Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.
机译:数据挖掘目前是信息和数据库技术领域的前沿研究主题。它被认为是最有前途的关键技术之一。数据挖掘涉及多种技术,例如数学统计,模糊理论,神经网络和人工智能,具有相对高的技术内容。实现也很困难。在本文中,我们研究了关联规则挖掘技术的基本概念,过程和算法。针对大规模的数据库应用程序,为了提高数据挖掘的效率,我们提出了一种基于群集的增量关联规则挖掘算法,即使用快速群集。首先,研究了实现性能评估数据采矿的可行性;然后,分析了实现信息系统所需的业务流程,设计了业务流程相关的链接和相应的数据输入接口,然后设计了实现数据处理的数据进程,包括数据基础和数据库模型。针对大规模数据库挖掘的高效率,数据库开发工具用于实现该算法的特定系统设置和程序设计。纳入了高校人力资源管理系统,他们进行了成功的协会广播,实现可视化,最终发现了有价值的信息。

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