Efficient Data Mining Techniques to Enhance Hr Activities
Introduction: Recently, research in Human Resource (HR) activities that are embedded with Data Mining Techniques can solve unstructured and indistinct decision making problems. Human Resource Management (HRM) activities can facilitate to take fair and consistent decisions, and to improve the effectiveness of decision-making processes. Besides the challenges for HR Professionals to manage the organizational decisions and talents, especially they have to ensure that the selection of a right person, providing training to develop the skills, positioning the right person at right job at the right time, assessing the performance and appraising the personnel. Basically, HRM is a comprehensive set of managerial activities and tasks concerned with developing and maintaining a competent workforce – human resource. HRM aims to facilitate organizational competitiveness; enhance productivity and quality; promote individual growth and development; and complying with legal and social obligation. Besides that, in any organizations, they need to compete effectively in term of cost, quality, service or innovation. All these depend on having enough right people, with the right skills, deployed in the appropriate locations at appropriate points in time. Nowadays, in HRM field among the challenges of HR Professionals are selecting the skilled persons, providing training to the recruited people, placing the person in a right position, and performance appraisal. These tasks involve a lot of managerial decision, and which it is sometime very uncertain and difficult to make the best decisions. In reality, current HR decision practices depend on various factors such as human experience, knowledge, preference and judgment. These factors can cause inconsistent, inaccurate, inequality and unforeseen decisions. As a result, especially in promoting individual growth and development, this situation can often make people feel injustice. Besides that, in future, this can influence
References: Information Systems, 2008. 3(5): p. 464-481. [2] DeNisi, A.S. and R.W. Griffin, Human Resource Management. 2005, New York: Houghton Mifflin Company. [3] A TP Track Research Report Talent Management: A State of the Art. 2005, Tower Perrin HR Services. [4] Stavrou-Costea, E., The challenges of human resource management towards organizational effectiveness A comparative study in Southern EU. Journal of European Industrial, 2005. 29(2): p. 112-134. [5] DeCenZo, D.A. and S.P. Robbins, Fundamentals of Human Resource Management. 8th Ed. ed. 2005, New York: John Wiley & Son.Inc. . [6] Okpara, J.O. and P. Wynn, Human resource management practices in a transition economy: Challenges and prospects. Management Research News, 2008. 31(1): p. 57-76. [7] Hooper, R.S., et al., Use of an Expert System in a personnel selection process. Expert Systems and Applications, 1998. 14(4): p. 425-432. [10] Chien, C.F. and L.F. Chen, Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems and Applications, 2008. 34(1): p. 380-290. [11] Tai, W.S. and C.C. Hsu (2005) A Realistic Personnel Selection Tool Based on Fuzzy Data Mining Method. http://www.atlantispress.com/php/download_papaer?id=46 9/1/2008. [12] Huang, L.C., et al. Applying fuzzy neural network in human resource selection system. in Proceeding NAFIPS '04. IEEE Annual Meeting of the Fuzzy information 2004. 2004. [14] Quintero, A., D. Konare, and S. Pierre, Prototyping an Intelligent Decision Support System for improving urban infrastructures management. European Journal of Operational Research, 2005. 162(3): p. 654-672. [15] Qian, Z., G.H. Huang, and C.W. Chan, Development of an intelligent decision support system for air pollution control at coal-fired power plants. Expert System with Applications, 2004. 26(3): p. 335-356. [16] Viademonte, S. and F. Burstein, From Knowledge Discovery to computational Intelligent : A Framework for Intelligent Decision Support System. 2006, London: Springer London. [18] Mehrabad, M.S. and M.F. Brojeny, The development of an expert system for effective selection and appointment of the jobs applicants in human resource management. Computers & Industrial Engineering, 2007. 53(2): p. 306-312. [19] Liao, S.-H., A knowledge-based architecture for implementing collaborative problem-solving methods in military e-training. Expert Systems and Applications, 2007. In Press(Corrected Proof). [20] Tung, K.Y., et al., Mining the Generation Xer 's job attitudes by artificial neural network and decision tree - empirical evidence in Taiwan. Expert Systems and Applications, 2005. 29(4): p. 783-794. [21] Chien, C.F. and L.F. Chen, Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing. IEEE Transactions on Semiconductor Manufacturing, 2007. 20(4): p. 528-541. [22] Huang, M.J., Y.L. Tsou, and S.C. Lee, Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 2006. 19(6): p. 396-403. [23] Glenzer, C., A conceptual model of an interorganizational intelligent meeting-scheduler (IIMS). Strategic Information Systems, 2003. 12(1): p. 47-70. [24] Bozbura, F.T., A. Beskese, and C. Kahraman, Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems and Applications, 2007. 32(4): p. 1100-1112. [25] Haddawy, P. and N.T.N. Hien (2007) A decision support system for evaluating international student applications. [26]. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference, pp 487–499 [27]. Ahmed S, Coenen F, Leng PH (2006) Tree-based partitioning of date for association rule mining. Knowl Inf Syst 10(3):315–331 [28]. Banerjee A, Merugu S, Dhillon I, Ghosh J (2005) Clustering with Bregman divergences. J Mach Learn Res 6:1705–1749 [29]. Bezdek JC, Chuah SK, Leep D (1986) Generalized k-nearest neighbor rules. Fuzzy Sets Syst 18(3). [30]. Bloch DA, Olshen RA, Walker MG (2002) Risk estimation for classification trees. J Comput Graph Stat 11:263–288 [31]. Bonchi F, Lucchese C (2006) On condensed representations of constrained frequent patterns. Knowl Inf Syst 9(2):180–201 [32]. Breiman L (1968) Probability theory. Addison-Wesley, Reading. Republished (1991) in Classics of mathematics.SIAM, Philadelphia [33] [34]. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont [35] [36]. Chen JR (2007) Making clustering in delay-vector space meaningful. Knowl Inf Syst 11(3):369–385 -----------------------