Constructing a Web-based Employee Training Expert System with Data Mining Approach

Kuang-Ku Chen, Mu-Yen Chen, Hui-Ju Wu, Yi-Lung Lee
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引用次数: 18

Abstract

Knowledge management (KM) is an important strategy in business management and competition in 21st century. Companies must manage their valuable knowledge and experience more aggressively to enhance competitive advantage and human resource management (HRM). In this paper, we present a web-based training system named ETES - employee training expert system and the methodologies of its implementation. ETES applied rule-based expert system technology to infer the learning type for employees. Moreover, ETES uses association rule mining to find training strategies and learning map for personal learning. Besides, ETES provides different training materials for employees according to their learning aptitudes, records and occupations. The system has been tested and is now in pilot use by Teraauto Corporation which is a high-profits listed securities company in Taiwan.
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基于数据挖掘的员工培训专家系统的构建
知识管理是21世纪企业管理和竞争的重要战略。公司必须更积极地管理他们宝贵的知识和经验,以增强竞争优势和人力资源管理(HRM)。本文提出了一个基于网络的培训系统——员工培训专家系统及其实现方法。ETES运用基于规则的专家系统技术对员工进行学习类型推断。此外,该算法还利用关联规则挖掘来寻找训练策略和个人学习地图。此外,公司还根据员工的学习能力、记录和职业,为员工提供不同的培训材料。该系统已经过测试,目前正在台湾一家高利润上市证券公司泰泰汽车公司试用。
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