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A Machine Learning Application for Human Resource Data Mining Problem

  • Zhen Xu
  • Binheng Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Apply machine learning methods to data mining domain can be more helpful to extract useful knowledge for problems with changing conditions. Human resource allocation is a kind of problem in data mining domain. It presents machine learning techniques to dissolve it. First, we construct a new model which optimizes the multi-objectives allocation problem by using fuzzy logic strategy. One of the most important problems in the model is how to get the precise individual capability matrixes. Machine learning method by being told is well used to settle the problem in this paper. In the model, appraisal values about employees are saved in knowledge warehouse. Before tasks allocation, machine learning approach provides the capability matrixes based on the existing data sets. Then Task-Arrange or Hungarian Algorithm provides the final solution with our proposed matrixes. After present tasks are finished, machine learning method by being told can update the matrixes according to the suggestions on employees’ performance provided by the specialists. Useful knowledge can be well mined in cycles by learning approach. As a numerical example demonstrated, it is helpful to make a realistic decision on human resource allocation under a dynamic environment for organizations.

Keywords

Machine Learning Method Satisfaction Degree Resource Allocation Algorithm Machine Learn Application Hungarian Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhen Xu
    • 1
  • Binheng Song
    • 2
  1. 1.School of SoftwareTsinghua UniversityBeijingP.R. China
  2. 2.Dept. MathTsinghua UniversityP.R. China

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