摘要
社会的发展要求企业更合理、高效的利用人力资源,数据挖掘技术可以从大量的历史数据中发现内在的规律和联系,为企业的人员安排提供决策依据。将决策树和模糊集合相结合,引入聚类中心和模糊隶属函数的概念用于人员效率数据挖掘中,并采用Min-Ambiguity算法提取出评估人员效率的基本规则,解决了数据模糊性和不确定性的问题,生成的知识表示方式自然、易于理解、并且具有更强的分类能力及稳健性。
The development of the society requires companies more reasonable and more efficient use of human resources, data mining can get some potential rules and hidden patterns from historical data, so as to guide the decision-making for the per-sonnel arrange of companies. This paper combines the decision tree with fuzzy theory, takes cluster center and fuzzy subordi-nate function into mining staff efficiency by using Min-Ambiguity to extract the basic rules of evaluation personnel efficiency to solve the ambiguity and uncertainty of data problems. The resulted knowledge is natural and easy to understand, and has a stronger classification ability and robustness.
出处
《机械研究与应用》
2016年第2期58-60,共3页
Mechanical Research & Application
基金
天津市支撑计划项目(编号:11ZCKFGX04100)
中央高校(编号:ZXH2012D003)
关键词
模糊决策树
数据挖掘
人员效率
fuzzy decision tree
data mining
staff efficiency
