A Machine Learning Application for Human Resource Data Mining Problem
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.
KeywordsMachine Learning Method Satisfaction Degree Resource Allocation Algorithm Machine Learn Application Hungarian Algorithm
Unable to display preview. Download preview PDF.
- 1.Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)Google Scholar
- 4.Grant Jr., E.W., Hendon Jr., F.N.: An application of linear programming in hospital resource allocation. J. Health Care Market. 1, 69–72 (1987)Google Scholar
- 7.Wang, C.: The Distributed Algorithms of Optimum Distribution for the Limited Resource. Journal of Northwest Normal University 30(1), 26–30 (1994)Google Scholar
- 8.Keyser Thomas, K., Robert, D.: Distributed computing approaches toward manufacturing scheduling problems. J. IIE Transactions 30(4), 379–390 (1998)Google Scholar
- 10.Omer, K., de Korvin, A.: Utilizing Fuzzy Logic in Decision-Making: New Frontiers. Application of Fuzzy Sets and Theory of Evidence to Accounting II, Stamford, Connecticut, pp. 3–14. JAI Press Inc., Greenwich (1998)Google Scholar
- 11.Cloete, I., van Zyl, J.: A Machine Learning Framework for Fuzzy Set Covering Algorithms. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 3199–3203 (2004)Google Scholar
- 12.Lu, K.: Single-objective, multi-objectives and Integer programming, pp. 171–180. Tsinghua University Press, Beijing (1999)Google Scholar
- 14.Zebda, A.: Fuzzy Set Theory and Behavioral Models for Decision Making under Ambiguity, Stamford, Connecticut. In: Application of Fuzzy Sets and the Theory of Evidence to Accounting II, pp. 15–27. JAI Press Inc., Green wich (1998)Google Scholar
- 15.Shaw, M.J.: Knowledge based scheduling in flexible manufacturing systems. In: TI Tech. J. pp. 54–61 (Winter 1987)Google Scholar