Clustering analysis based on improved k-means algorithm and its application in HRM system

被引：0
|
作者：
Liu, Yanli [1 ]
Liu, Xiyu [1 ]
Meng, Yan [1 ]
机构：
[1] Shandong Normal Univ, Sch Management & Econ, Jinan, Peoples R China
来源：
PROCEEDINGS OF THE 2007 1ST INTERNATIONAL SYMPOSIUM ON INFORMATION TECHNOLOGIES AND APPLICATIONS IN EDUCATION (ISITAE 2007) | 2007年
关键词：
k-means; algorithm; density; clustering center; feature weight;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Along which the arrival of the knowledge-based economy, talented person's strategy becomes the source of enterprise core competencies more and more. It is the key to find and to choose high feature and creative persons for the human resource development and management. An improved K-means clustering algorithm is brought forward, based on basic K-means Algorithm, adopts a method grounded on density to choose original clustering centers and feature weight learning to improve chastening result. It overcomes the shortcomings of the difficulty to choose original clustering centers and unstable cloistering result. Then the cloistering analysis model of Personal management system is put forward based on improved K-means clustering algorithm. With the use of SQL Server 2000, the realization of the model has been successfully used in the human resource management of a famous domestic software company and offers a useful reference for the enterprise to select and appoint talented persons.
引用
收藏
页码：473 / 477
页数：5
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