Domain driven data mining in human resource management: A review of current research
► We review data mining in human resource management as a new and prospering field of research using a domain driven framework. ► Findings show a large number of contributions of recent date and mostly technical or methodical provenance (n=100). ► Contributions cover a broad variety of HR domain pro...
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| Published in | Expert systems with applications Vol. 40; no. 7; pp. 2410 - 2420 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier Ltd
01.06.2013
Elsevier |
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| Online Access | Get full text |
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| Abstract | ► We review data mining in human resource management as a new and prospering field of research using a domain driven framework. ► Findings show a large number of contributions of recent date and mostly technical or methodical provenance (n=100). ► Contributions cover a broad variety of HR domain problems and data mining methods. ► Contributions, however, regularly do not consider certain specific domain requirements. ► We elaborate on these requirements to support future work on the topic.
An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new research field. The current paper reviews this research on HR data mining to systematically uncover recent advancements and suggest areas for future work. Based on the approach of domain driven data mining, an initial framework with significant domain-specific requirements is elaborated. Relevant research contributions are identified and reviewed against the background of this framework. The review reveals that HRM constitutes a noteworthy new domain of data mining research that is dominated by method- and technology-oriented work. However, specific domain requirements, such as evaluating the domain success or complying with legal standards, are frequently not recognized or considered in current research. Therefore, the systematic consideration of domain-specific requirements is demonstrated here to have significant implications for future research on data mining in HRM. |
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| AbstractList | An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new research field. The current paper reviews this research on HR data mining to systematically uncover recent advancements and suggest areas for future work. Based on the approach of domain driven data mining, an initial framework with significant domain-specific requirements is elaborated. Relevant research contributions are identified and reviewed against the background of this framework. The review reveals that HRM constitutes a noteworthy new domain of data mining research that is dominated by method- and technology-oriented work. However, specific domain requirements, such as evaluating the domain success or complying with legal standards, are frequently not recognized or considered in current research. Therefore, the systematic consideration of domain-specific requirements is demonstrated here to have significant implications for future research on data mining in HRM. ► We review data mining in human resource management as a new and prospering field of research using a domain driven framework. ► Findings show a large number of contributions of recent date and mostly technical or methodical provenance (n=100). ► Contributions cover a broad variety of HR domain problems and data mining methods. ► Contributions, however, regularly do not consider certain specific domain requirements. ► We elaborate on these requirements to support future work on the topic. An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new research field. The current paper reviews this research on HR data mining to systematically uncover recent advancements and suggest areas for future work. Based on the approach of domain driven data mining, an initial framework with significant domain-specific requirements is elaborated. Relevant research contributions are identified and reviewed against the background of this framework. The review reveals that HRM constitutes a noteworthy new domain of data mining research that is dominated by method- and technology-oriented work. However, specific domain requirements, such as evaluating the domain success or complying with legal standards, are frequently not recognized or considered in current research. Therefore, the systematic consideration of domain-specific requirements is demonstrated here to have significant implications for future research on data mining in HRM. |
| Author | Strohmeier, Stefan Piazza, Franca |
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| Cites_doi | 10.1145/1401890.1401959 10.1109/JCPC.2009.5420070 10.1504/IJBIS.2010.030629 10.2991/jcis.2006.46 10.1109/NABIC.2009.5393596 10.5120/823-1165 10.1109/FUZZY.2009.5277379 10.1109/ARTCom.2009.214 10.1007/s10696-010-9068-x 10.1108/02621711111126846 10.1016/j.eswa.2005.06.012 10.1109/PACCS.2011.5990180 10.1109/ICMA.2009.5246570 10.1016/j.dss.2010.08.006 10.1111/1468-0394.00235 10.1109/FITME.2008.133 10.1057/mel.2011.3 10.3233/FI-2009-0026 10.1111/j.1467-9310.2005.00408.x 10.1007/978-3-540-76631-5_94 10.1016/j.eswa.2011.01.001 10.1300/J017v19v04_05 10.5539/cis.v4n3p67 10.1016/j.cor.2004.06.022 10.1016/j.eswa.2010.10.003 10.1007/11686699_37 10.1109/CIDM.2009.4938650 10.17705/1CAIS.00506 10.3233/978-1-60750-633-1-49 10.1109/CIMSA.2006.250749 10.1109/PEITS.2009.5407033 10.1016/j.eswa.2009.05.064 10.1007/11678564_16 10.1080/09585192.2011.610340 10.1109/PICMET.2008.4599705 10.1007/978-3-642-13318-3_66 10.1097/00024665-200407000-00012 10.1109/APCIP.2009.89 10.1108/09600030310469153 10.1109/ISISE.2009.55 10.1109/CSCWD.2005.194143 10.1142/S0218001407005612 10.1109/WiCom.2008.2687 10.1007/978-3-642-15387-7_53 10.1016/j.clsr.2006.03.004 10.1016/j.eswa.2008.03.021 10.1057/palgrave.jors.2602516 10.1109/IITA.Workshops.2008.235 10.1016/j.eswa.2009.07.037 10.1109/IFCSTA.2009.90 10.1016/j.eswa.2006.09.003 10.1016/j.eswa.2010.07.134 10.1109/ICDE.1995.380415 10.1109/ISBIM.2008.187 10.1007/11548706_51 10.1016/j.eswa.2008.07.052 10.1007/978-3-642-23878-9_55 10.1007/s10845-008-0145-x 10.1007/978-3-540-77018-3_2 10.1109/ICICTA.2009.235 10.1504/IJPQM.2011.042511 10.1504/IJBIS.2008.018597 10.1109/TSMCC.2010.2053532 10.1007/11539087_85 10.1109/ISCID.2009.220 10.1109/ISDA.2009.64 10.1109/ICMLC.2004.1382104 10.3233/IDA-2008-12206 10.1007/978-3-540-88875-8_86 10.1109/WI.2005.99 10.1016/S0957-4174(03)00136-2 10.1109/ASONAM.2009.52 10.1016/j.eswa.2008.02.021 10.1109/TKDE.2010.32 10.1109/ICEOE.2011.6013123 10.1109/BICTA.2009.5338100 10.1109/ICIII.2010.200 10.1016/j.eswa.2008.02.069 10.1109/WI.2003.1241181 10.1109/TSM.2007.907630 10.4304/jcp.6.1.139-146 10.1109/ICICISYS.2010.5658634 10.1109/AINA.2010.168 10.1109/EBISS.2010.5473417 10.1016/j.clsr.2010.11.009 10.1007/978-3-642-13422-7_4 10.1109/ICMLC.2008.4620484 10.1145/1571941.1571991 10.1109/CAIA.1995.378795 10.1007/978-3-540-45226-3_56 10.1109/FSKD.2010.5569250 10.1109/ICSMC.2005.1571205 10.1109/IUCE.2009.63 |
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| Keywords | Literature review HRM Data mining Electronic human resource management (e-HRM) HR data mining Domain driven data mining Data analysis Data processing Information extraction Data driven modelling Human resource management Standards |
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| References | Sivaram, Ramar (b0560) 2010; 4 (pp. 275–278). Karahoca, A., Karahoca, D., & Kaya, O. (2008). Data mining to cluster human performance by using online self regulating clustering method. In Chen, Ye, Williams, Xu (b0095) 2007; Vol. 4819 Khosla, R., Lai, C., & Chu, M.-T. (2006). Measurement and comparison of non-verbal behavior and cognitive behavior in human resource management – Application in sales recruitment. In [89]. Zhang, X., Zhu, Y., & Hua, N. (2010). Improved paralled algorithm for mining frequent itemset used in HRM. 2010 (pp. 309–312). (pp. 2334–2339). Li, X.-H., Zhao, J.-H., & Li, B.-H. (2004). Investigation of fuzzy mathematical model for general evaluation of administrators in university. In Wu, Lee, Tzeng (b0650) 2005; Vol. 3642 Kessler, Béchet, Torres-Moreno, Roche, El-Bèze (b0325) 2009 (pp. 223–230). [100]. Coppock (b0125) 2003 [16]. Kroll (b0350) 2001; 83 (pp. 560–565). Zhang, Segall (b0695) 2010 Retrieved from . [54]. 8 [92]. (pp. 9–16). Huang, Y. (2009). Study of college human resources data mining based on the SOM algorithm. In (pp. 283–290). [86]. (pp. 257–259). [27]. Clyde, S., Zhang, J., & Yao, C.-C. (1995). An object-oriented implementation of an adaptive classification of job openings. In Chen, X., & Wang, F. (2010). Application of data mining on enterprise human resource performance management. In [D]. [65]. (pp. 1–4). [80]. (pp. 1–11). [56]. Zhao, X. (2008). A study of performance evaluation of HRM: based on data mining. In Wahlstrom, K., Roddick, J. F., Sarre, R., Estivill-Castro, V., & DeVries, D. (2002). On the ethics of data mining. Technical Report KDM-02-006. Retrieved from Flinders University of South Australia, Knowledge Discovery and Management Laboratory website Delgado-Gómez, Aguado, Lopez-Castroman, Santacruz, Artés-Rodriguez (b0130) 2011; 38 Han, J., & Dong, Y. (2007). Application of association rules based on rough set in human resource management. In Fancello, Pani, Pisano, Serra, Zuddas, Fadda (b0160) 2011; 13 Zhang, X., Zhu, Y., & Hua, N. (2009). Privacy parallel algorithm for mining association rules and its application in HRM. In Sharma, Osei-Bryson (b0555) 2009; 36 Ning, C. (2010). The application of neural network to the allocation of enterprise human resources. In Rupnik, Jaklič (b0530) 2009 Baker, Yacef (b0030) 2009; 1 Tai, W.-S., & Hsu, C.-C. (2006). A realistic personnel selection tool based on fuzzy data mining method. In Teodorescu, R., Teodorescu, N., & Gavrila, C. (2009). Bayesian networks in outplacement. Retrieved from (pp. 372–375). Aiolli, F., de Filippo, M., & Sperduti, A. (2009). Application of the preference learning model to a human resources selection task. In [53]. [99]. Kirby, E., Dufner, D., & Palmer, J. (1998). An analysis of applying artificial neural networks for employee selection. In Honkela, T., Nordfors, R., & Tuuli, R. (2004). Document maps for competence management. In Wu (b0645) 2009; 36 [91]. Ye, J., Hu, H., & Chai, C. (2009). A talent classification method based on SVM. In [26]. (pp. 214–218). (pp. 214–221). [41]. (pp. 1–3). Wang, Z., Li, S., Wang, Y., & Li, S. (2009). The research of task assignment based on ant colony algorithm. In (pp. 44–49). Isaias, P., Casaca, C. & Pifano, S. (2010). Recommender systems for human resources task assignment. In (IDEAL ’11) (pp. 463–473). [72]. (pp. 31–39). [29]. Wang, Q., Li, B., & Hu, J. (2009a). Feature selection for human resource selection based on affinity propagation and SVM sensitivity analysis. In [37]. Wang, Wang (b0615) 2009; 90 [14]. [98]. Yang, S.-M. (2008). A self-adapting regression SVM model and its application on middle managers performance evaluation. In Quinn, Rycraft, Schoech (b0495) 2002; 19 Jantan, Hamdan, Othman (b0275) 2010; Vol. 6276 (pp. 198–203). [42]. (pp. 324–327). Ngai, Xiu, Chau (b0465) 2009; 36 Gkoulalas-Divanis, Verykios (b0750) 2010 Jantan, Hamdan, Othman (b0265) 2009; 26 Liu, Y., Kou, Z., Perlich, C., & Lawrence, R. (2008). Intelligent system for workforce classification. In Kao, W.-H., Huang, I.-M., Shen, H.-S., & Lin, C.-C. (2009). A study of weighted association rule applied in human resource job requirement. In [40]. Han, J. (2009). Application of fuzzy data mining algorithm in performance evaluation of human resource. In Zhang, H. (2009). Fuzzy evaluation on the performance of human resources management of commercial banks based on improved algorithm. In Strohmeier, Piazza (b0570) 2010; 5 Luell, J. (2010). Employee fraud detection under real world conditions. Doctoral dissertation, University of Zurich, Retrieved from (pp. 203–210). [1]. Hong, Pai, Huang, Yang (b0220) 2005; Vol. 3610 Juan (b0295) 2009 71 (pp. 343–346). Hsiung, Yang (b0235) 2012; 23 Jindal, Singla (b0290) 2011; 1 (pp. 1977–1980). Bernik, M., Flojancic, J., Crnigoj, D., & Bernik I. (2007). Using information technology for human resource management decisions. In Tzeng, Hsieh, Lin (b0605) 2004; 22 Sáiz, L., Pérez, A., Herrero, A., & Corchado, E. (2011). Analyzing key factors of human resources management. In Hou, Dong, Liu, Gu (b0230) 2007; 2007 Jantan, Hamdan, Othman (b0285) 2010; 5 (pp. 28-1–28-7). [51]. [97]. Imai, S., Lin, C.-W., Watada, J., & Tzeng, G.-H. (2008a). Knowledge acquisition in human resource management based on rough sets. In (pp. 151–153). Han, J., Ren, J., & Dong, Y. (2008). Research on time series model for human resource management based on web technique. In Youzheng, C., & Ming, G. (2008). Data mining to improve human resource in construction company. In Li, Y. (2011). Research of evaluation staff satisfaction based on BP neural network. In (pp. 124–130). Pechenizkiy, Puuronen, Tsymbal (b0475) 2008; 12 (pp. 57–62). (pp. 49–45). Schermer (b0545) 2011; 27 (pp. 3–14). Lee, W. C., & Drasgow, F. (2001). Predicting employee retention: an examination of a retention likelihood scale (Report No. 45). Retrieved from Center for Human Resource Management website Adejuwon, Mosavi (b0005) 2010; 7 Puuronen, S., & Pechenizkiy, M. (2010). Towards the generic framework for utility considerations in data mining research. In Färber (b0745) 2005 Imai, Lin, Watada, Tzeng (b0255) 2008; 9 [85]. [24]. Färber, F., Weitzel, T., & Keim, T. (2003). An automated recommendation approach to selection in personnel recruitment. In Kessler, Béchet, Roche, El-Bèze, Torres-Moreno (b0320) 2008; Vol. 5333 (b0315) 2010 Nagadevara, Srinivasan, Valk (b0455) 2008; 16 Chien, Chen (b0105) 2008; 34 Liu, H., Dai, S., & Jiang, H. (2009). Application of rough set and support vector machine in competency assessment. In Choudhary, Harding, Tiwari (b0115) 2008; 20 Hermiz (b0210) 1999 (pp. 2830–2833). Lockamy, Service (b0410) 2011; 30 Wu, Ting, Chang (b0640) 2010 Mohanraj, V., Lakshmipathi, R., Chandrasekaran, M., Senthilkumar, J., & Suresh, Y. (2009). Intelligent agent based talent evaluation engine using a knowledge base. In Chien, Chen (b0740) 2007; 20 Zhu, J., Goncalves, A. L., Uren, V. S., Motta, E., & Pacheco, R. (2005). Mining web data for competency management. In Burgard, Piazza (b0735) 2009 Lakshmipathi, Chandrasekaran, Mohanraj, Senthilkumar, Suresh (b0355) 2010; 2 Li, Ge, Yin (b0375) 2011 [35]. [96]. Rok, Matjaž, Marjan (b0520) 2007; 47 (pp. 280–285). [5]. Long, Troutt (b0765) 2003 Bole, U., Jakličb, J., Papac, G., & Žabkard, J. (2011). The critical success factors of data mining in organizations. Retrieved from (pp. 76–77). [46]. Romero, Ventura (b0525) 2010; 40 (pp. 31–36). Lescreve, F. J. (1999). Data integration and classification for an officer selection system. In Devanna, Fombrun, Tichy (b0135) 1984 [84]. (pp. 130–133). [6]. Drigas, Kouremenos, Vrettos, Vrettaros, Kouremenos (b0150) 2004; 26 Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In (pp. 296–299). (pp. 2407–2413). [25]. Ye, P. (2011). The decision tree classification and its application research in personnel management. In (pp. 645–648). Ngai, Hu, Wong, Chen, Sun (b0460) 2011; 50 (pp. 744–750). Ramesh (b0500) 2001; 5 Huang, Wu, Lee (b0240) 2008; 59 [57]. (pp. 94–100). Rodrigues, S., Oliveira, J., & de Souza, J. M. (2005). Competence mining for team formation and virtual community recommendation. In (pp. 115–126). [17]. Markham (b0430) 2011; 8 Moehrle, Walter, Geritz, Müller (b0440) 2005; 35 (pp. 111–117). Min, Emam (b0435) 2003; 33 Li, W., Xu, S., & Meng, W. (2009). A risk prediction model of construction enterprise human resources based on support vector machine. In (pp. 82–85). Ly, Rinderle, Dadam, Reichert (b0425) 2006; Vol. 3812 (pp. 839–844). Cho, Ngai (b0110) 2003; 20 [22]. (pp. 2329–2339). [21]. [45]. Kessler, Torres-Moreno, El-Bèze (b0330) 2007; Vol. 4827 Sugimori, Iida, Suka, Ichimura, Yoshida (b0575) 2003; Vol. 2774 Zhang, Deng (b0685) 2011; 6 (pp. 349–351). Goonawardene, Subashini, Boralessa, Premaratne (b0190) 2010; Vol. 6064 Holland, A., & Fathi, M. (2005). Creating graphical models as representation of personalized skill profiles. In Pedreshi, D., Ruggieri, S., & Turini, F. (2008). Discrimination-aware data mining. In Zhao, X. (2008). An empirical study of data mining in performance evaluation of HRM. In (pp. 18–26). [58]. Beccera-Fernandez, I., & Rodriguez, J. (2001). Web data mining techniques for expertise-locator knowledge management systems. In (2001). [49]. (pp. 407–419). Weiss, G. M. (2009). Data mining in the real world: Experiences, challenges, and recommendations. In [10]. [79]. [33]. Yang, Lin, Chen, Shi (b0660) 2009; 36 [94]. (pp. 411–416). Tung, Huang, Chen, Shih (b0595) 2005; 29 Li, Y.-M., Lai, C.-Y., & Kao, C.-P. (2008). Incorporate personality trait with support vector machine to acquire quality matching of personnel recruitment. In Tung, Tseng, Weng, Lee, Liao, Tsai (b0600) 2010; 37 Al-Zegaier, Al-Zu’bi, Barakat (b0020) 2011; 4 Wong, T.-L., Lam, W., & Chen, B. (2009). Mining employment market via text block detection and adaptive cross-domain information extraction. In Bal, M., Bal, Y., & Ustundag, A. (2011). Knowledge representation and discovery using formal concept analysis: An HRM application. In The World congress on engineering (pp. 1068–1073). [4]. Chang (b0075) 2009; 3 [44]. [67]. (pp. 945–948). Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Sexton, McMurtrey, Micha 10.1016/j.eswa.2012.10.059_b0715 Hsiung (10.1016/j.eswa.2012.10.059_b0235) 2012; 23 10.1016/j.eswa.2012.10.059_b0675 10.1016/j.eswa.2012.10.059_b0710 10.1016/j.eswa.2012.10.059_b0395 10.1016/j.eswa.2012.10.059_b0670 10.1016/j.eswa.2012.10.059_b0310 10.1016/j.eswa.2012.10.059_b0040 Chen (10.1016/j.eswa.2012.10.059_b0085) 2011; 23 Kessler (10.1016/j.eswa.2012.10.059_b0320) 2008; Vol. 5333 Nagadevara (10.1016/j.eswa.2012.10.059_b0450) 2008; 16 Rok (10.1016/j.eswa.2012.10.059_b0520) 2007; 47 Strohmeier (10.1016/j.eswa.2012.10.059_b0570) 2010; 5 Lakshmipathi (10.1016/j.eswa.2012.10.059_b0355) 2010; 2 Goonawardene (10.1016/j.eswa.2012.10.059_b0190) 2010; Vol. 6064 Moehrle (10.1016/j.eswa.2012.10.059_b0440) 2005; 35 Kroll (10.1016/j.eswa.2012.10.059_b0350) 2001; 83 10.1016/j.eswa.2012.10.059_b0700 10.1016/j.eswa.2012.10.059_b0300 Huang (10.1016/j.eswa.2012.10.059_b0240) 2008; 59 10.1016/j.eswa.2012.10.059_b0785 10.1016/j.eswa.2012.10.059_b0665 10.1016/j.eswa.2012.10.059_b0780 10.1016/j.eswa.2012.10.059_b0385 Wu (10.1016/j.eswa.2012.10.059_b0645) 2009; 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| References_xml | – reference: (pp. 18–26). [58]. – volume: 22 start-page: 206 year: 2006 end-page: 217 ident: b0485 article-title: EU data protection policy. The directive 95/46/EC: Ten years after publication-title: Computer Law and Security Review – reference: Lescreve, F. J. (1999). Data integration and classification for an officer selection system. In – start-page: 366 year: 2003 end-page: 381 ident: b0765 article-title: Data mining for human resource information systems publication-title: Data mining: Opportunities and challenges – start-page: 223 year: 2009 end-page: 229 ident: b0735 article-title: Data warehouse and business intelligence systems in the context of e-HRM publication-title: Encyclopedia of HRIS: Challenges in e-HRM – reference: Li, W., Xu, S., & Meng, W. (2009). A risk prediction model of construction enterprise human resources based on support vector machine. In – volume: Vol. 4827 start-page: 985 year: 2007 end-page: 995 ident: b0330 article-title: E-Gen: Automatic job offer processing system for human resources publication-title: Lecture notes in computer science – volume: Vol. 5333 start-page: 625 year: 2008 end-page: 634 ident: b0320 article-title: Automatic profiling system for ranking candidates answers in human resources publication-title: Lecture notes in computer science – reference: Li, Y. (2011). Research of evaluation staff satisfaction based on BP neural network. In – volume: 38 start-page: 1999 year: 2011 end-page: 2006 ident: b0540 article-title: Employee churn prediction publication-title: Expert Systems with Applications – volume: 5 start-page: 1 year: 2001 end-page: 36 ident: b0500 article-title: GPR: A data mining tool using genetic programming publication-title: Communications of the Association for Information Systems – reference: Tai, W.-S., & Hsu, C.-C. (2006). A realistic personnel selection tool based on fuzzy data mining method. In – reference: . Retrieved from – volume: Vol. 3812 start-page: 177 year: 2006 end-page: 190 ident: b0425 article-title: Mining staff assignment rules from event-based data publication-title: Lecture Notes in Computer Science – reference: Zhang, X., Zhu, Y., & Hua, N. (2009). Privacy parallel algorithm for mining association rules and its application in HRM. In – reference: . [67]. – reference: (pp. 2830–2833). – reference: Wong, T.-L., Lam, W., & Chen, B. (2009). Mining employment market via text block detection and adaptive cross-domain information extraction. In – volume: 3 start-page: 417 year: 2009 end-page: 426 ident: b0075 article-title: Employee turnover: A novel prediction solution with effective feature selection publication-title: WSEAS Transactions on Information Science and Applications – reference: Puuronen, S., & Pechenizkiy, M. (2010). Towards the generic framework for utility considerations in data mining research. In – volume: Vol. 3865 start-page: 365 year: 2006 end-page: 374 ident: b0515 article-title: Recommendation for team and virtual community formations based on competence mining publication-title: Lecture notes in computer science – volume: 37 start-page: 434 year: 2010 end-page: 439 ident: b0365 article-title: Exploring high-performers’ required competencies publication-title: Expert Systems with Applications – reference: . [93]. – reference: (pp. 3–14). – reference: Honkela, T., Nordfors, R., & Tuuli, R. (2004). Document maps for competence management. In – reference: Li, Y.-M., Lai, C.-Y., & Kao, C.-P. (2008). Incorporate personality trait with support vector machine to acquire quality matching of personnel recruitment. In – reference: Lee, W. C., & Drasgow, F. (2001). Predicting employee retention: an examination of a retention likelihood scale (Report No. 45). Retrieved from Center for Human Resource Management website: – reference: . [78]. – volume: 26 start-page: 775 year: 2009 end-page: 783 ident: b0265 article-title: Knowledge discovery techniques for talent forecasting in human resource application publication-title: World Academy of Science, Engineering and Technology – reference: . [D]. – volume: 2 start-page: 2526 year: 2010 end-page: 2534 ident: b0280 article-title: Human talent prediction in HRM using c4.5 classification algorithm publication-title: International Journal on Computer Science and Engineering – reference: Ning, C. (2010). The application of neural network to the allocation of enterprise human resources. In – reference: Wahlstrom, K., Roddick, J. F., Sarre, R., Estivill-Castro, V., & DeVries, D. (2002). On the ethics of data mining. Technical Report KDM-02-006. Retrieved from Flinders University of South Australia, Knowledge Discovery and Management Laboratory website: – reference: (pp. 57–62). – reference: . [40]. – reference: Bernik, M., Flojancic, J., Crnigoj, D., & Bernik I. (2007). Using information technology for human resource management decisions. In – year: 2010 ident: b0315 publication-title: Human resource information systems: Basics, applications and future directions – reference: Kane-Sellers, M. L. (2007). Predictive models of employee voluntary turnover in a North American professional sales force using data-mining analysis. Doctoral dissertation, Texas A&M University. Retrieved from – reference: . [35]. – reference: Teodorescu, R., Teodorescu, N., & Gavrila, C. (2009). Bayesian networks in outplacement. Retrieved from – reference: Rodrigues, S., Oliveira, J., & de Souza, J. M. (2005). Competence mining for team formation and virtual community recommendation. In – volume: 90 start-page: 395 year: 2009 end-page: 426 ident: b0615 article-title: 3DM: Domain-oriented data-driven data mining publication-title: Fundamenta Informaticae – reference: Youzheng, C., & Ming, G. (2008). Data mining to improve human resource in construction company. In – volume: 6 start-page: 139 year: 2011 end-page: 146 ident: b0685 article-title: The data mining of the human resources data warehouse in university based on association rule publication-title: Journal of Computers – volume: 5 start-page: 186 year: 2010 end-page: 197 ident: b0570 article-title: Informating HRM: A comparison of data querying and data mining publication-title: International Journal of Business Information Systems – volume: 37 start-page: 2427 year: 2010 end-page: 2438 ident: b0600 article-title: A rule-based CBR approach for expert finding and problem diagnosis publication-title: Expert Systems with Applications – reference: . [92]. – start-page: 219 year: 2011 end-page: 224 ident: b0375 article-title: Research on talent introduction hazard and training strategy of university based on data mining publication-title: modeling risk management for resources and environment in China – reference: Wang, Q., Li, B., & Hu, J. (2009a). Feature selection for human resource selection based on affinity propagation and SVM sensitivity analysis. In – volume: 36 start-page: 3378 year: 2009 end-page: 3387 ident: b0660 article-title: Improving scheduling of emergency physicians using data mining analysis publication-title: Expert Systems with Applications – reference: (pp. 839–844). – volume: Vol. 3642 start-page: 484 year: 2005 end-page: 494 ident: b0650 article-title: Simplifying the manager competency model by using the rough set approach publication-title: Lecture notes in computer science – start-page: 1245 year: 2010 end-page: 1268 ident: b0695 article-title: Commercial data mining software publication-title: Data Mining and Knowledge Discovery Handbook – volume: Vol. 3610 start-page: 668 year: 2005 end-page: 674 ident: b0220 article-title: Application of support vector machines in predicting employee turnover based on job performance publication-title: Lecture notes in computer science – reference: . [45]. – reference: (pp. 257–259). – reference: . [57]. – reference: Holland, A., & Fathi, M. (2005). Creating graphical models as representation of personalized skill profiles. In – volume: 32 start-page: 2635 year: 2005 end-page: 2651 ident: b0550 article-title: Employee turnover: A neural network solution publication-title: Computers and Operations Research – reference: Jantan, H., Hamdan, A. R., & Othman, Z. A. (2009b). Potential data mining classification techniques for academic talent forecasting. In – reference: (pp. 372–375). – volume: 36 start-page: 2592 year: 2009 end-page: 2602 ident: b0465 article-title: Application of data mining techniques in customer relationship management: A literature review and classification publication-title: Expert Systems with Applications – reference: Imai, S., Lin, C.-W., Watada, J., & Tzeng, G.-H. (2008a). Knowledge acquisition in human resource management based on rough sets. In – reference: . [10]. – reference: (pp. 560–568). – reference: (pp. 1173–1178). – reference: (pp. 1–4). – volume: 22 start-page: 755 year: 2010 end-page: 769 ident: b0065 article-title: Domain-driven data mining: Challenges and prospects publication-title: IEEE Transactions on Knowledge and Data Engineering – reference: (pp. 296–299). – volume: 36 start-page: 9574 year: 2009 end-page: 9579 ident: b0645 article-title: Exploring core competencies for R&D technical professionals publication-title: Expert Systems with Applications – volume: 1 start-page: 3 year: 2009 end-page: 17 ident: b0030 article-title: The state of educational data mining in 2009: A review and future visions publication-title: Journal of Educational Data Mining – reference: Bole, U., Jakličb, J., Papac, G., & Žabkard, J. (2011). The critical success factors of data mining in organizations. Retrieved from – reference: (pp. 9–16). – reference: Zhang, H. (2009). Fuzzy evaluation on the performance of human resources management of commercial banks based on improved algorithm. In – start-page: 375 year: 2009 end-page: 388 ident: b0530 article-title: The deployment of data mining into operational business processes publication-title: Data mining and knowledge discovery in real life applications – reference: (pp. 111–117). – reference: Zhao, X. (2008). An empirical study of data mining in performance evaluation of HRM. In – reference: . [37]. – reference: . [24]. – reference: (pp. 1–3). – reference: Zhao, X. (2008). A study of performance evaluation of HRM: based on data mining. In – volume: Vol. 6064 start-page: 532 year: 2010 end-page: 539 ident: b0190 article-title: A neural network based model for project risk and talent management publication-title: Lecture notes in computer science – volume: 4 start-page: 23 year: 2010 end-page: 28 ident: b0560 article-title: Applicability of clustering and classification algorithms for recruitment data mining publication-title: International Journal of Computer Applications – volume: 1 start-page: 108 year: 2011 end-page: 121 ident: b0290 article-title: Decision support system in human resource management (a study of HR intelligent techniques) publication-title: International Journal of Research in IT, Management and Engineering – volume: 7 start-page: 41 year: 2010 end-page: 44 ident: b0005 article-title: Domain driven data mining – Application to business publication-title: International Journal of Computer Science Issues – reference: Zhang, X., Zhu, Y., & Hua, N. (2010). Improved paralled algorithm for mining frequent itemset used in HRM. 2010 – volume: 8 start-page: 313 year: 2011 end-page: 332 ident: b0430 article-title: Assessing the prediction of employee productivity: A comparison of OLS vs. CART publication-title: International Journal of Productivity and Quality Management – reference: Khosla, R., Lai, C., & Chu, M.-T. (2006). Measurement and comparison of non-verbal behavior and cognitive behavior in human resource management – Application in sales recruitment. In – reference: Zhu, J., Goncalves, A. L., Uren, V. S., Motta, E., & Pacheco, R. (2005). Mining web data for competency management. In – reference: . [26]. – reference: (pp. 49–45). – start-page: 431 year: 2009 end-page: 441 ident: b0325 article-title: Job offer management: How improve the ranking of candidates publication-title: Foundations of intelligent systems – reference: (pp. 280–285). [5]. – reference: Liu, H., Dai, S., & Jiang, H. (2009). Application of rough set and support vector machine in competency assessment. In – volume: Vol. 6276 start-page: 491 year: 2010 end-page: 500 ident: b0275 article-title: Classification and prediction of academic talent using data mining techniques publication-title: Lecture notes in computer science – volume: 5 start-page: 694 year: 2010 end-page: 702 ident: b0285 article-title: Knowledge discovery techniques for talent forecasting in human resource application publication-title: Journal of Human and Social Sciences – year: 1999 ident: b0210 article-title: Critical success factors for data mining projects – reference: (pp. 223–230). [100]. – reference: . [14]. – reference: (IDEAL ’11) (pp. 463–473). [72]. – volume: 22 start-page: 232 year: 2004 end-page: 242 ident: b0605 article-title: Predicting nurses’ intention to quit with a support vector machine: A new approach to set up an early warning mechanism in human resource management publication-title: Computers, Informatics, Nursing – reference: Kirby, E., Dufner, D., & Palmer, J. (1998). An analysis of applying artificial neural networks for employee selection. In – volume: 40 start-page: 601 year: 2010 end-page: 618 ident: b0525 article-title: Educational data mining: A review of the state of the art publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews – reference: Wu, X., & Zhang, H. (2010). A talent markets analysis method based on data mining. In – reference: Feng, D., & Gao, Y. (2009). A HRD evaluation method based on BP neural network. In – volume: 20 start-page: 501 year: 2008 end-page: 521 ident: b0115 article-title: Data mining in manufacturing: A review based on the kind of knowledge publication-title: Journal of Intelligent Manufacturing – reference: Han, J. (2009). Application of fuzzy data mining algorithm in performance evaluation of human resource. In – reference: Han, J., & Dong, Y. (2007). Application of association rules based on rough set in human resource management. In – volume: 34 start-page: 280 year: 2008 end-page: 290 ident: b0105 article-title: Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry publication-title: Expert Systems with Applications – reference: Chang, H.-T., Wu, H.-J., & Ting, I.-H. (2009). Mining organizational networks for layoff prediction model construction. In – reference: Chen, X., & Wang, F. (2010). Application of data mining on enterprise human resource performance management. In – reference: (pp. 31–39). [29]. – reference: Luell, J. (2010). Employee fraud detection under real world conditions. Doctoral dissertation, University of Zurich, Retrieved from – reference: Aiolli, F., de Filippo, M., & Sperduti, A. (2009). Application of the preference learning model to a human resources selection task. In – volume: 16 start-page: 2020 year: 2008 end-page: 2032 ident: b0450 article-title: Early prediction of employee attrition in software companies – Application of data mining techniques publication-title: Research and Practice in Human Resource Management – volume: 16 start-page: 81 year: 2008 end-page: 99 ident: b0455 article-title: Establishing a link between employee turnover and withdrawal behaviours: Application of data mining techniques publication-title: Research and Practice in Human Resource Management – volume: 50 start-page: 559 year: 2011 end-page: 569 ident: b0460 article-title: The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature publication-title: Decision Support Systems – reference: . [16]. – reference: (pp. 343–346). – reference: . – volume: 26 start-page: 217 year: 2004 end-page: 224 ident: b0150 article-title: An expert system for job matching of the unemployed publication-title: Expert Systems with Applications – reference: (2001). [49]. – reference: Ziebarth, S., Malzahn, N., & Hoppe, H. U. (2009). Using data mining techniques to support the creation of competence ontologies. In – reference: . [27]. – reference: Ye, P. (2011). The decision tree classification and its application research in personnel management. In – start-page: 197 year: 2009 end-page: 201 ident: b0295 article-title: Early warning model research of state-owned enterprises’ human capital risks based on improved neural network publication-title: International Conference on Future BioMedical Information Engineering – reference: (pp. 969–974). – reference: (pp. 309–312). – reference: (pp. 28-1–28-7). [51]. – reference: (pp. 31–36). – reference: Yang, S.-M. (2008). A self-adapting regression SVM model and its application on middle managers performance evaluation. In – volume: 27 start-page: 45 year: 2011 end-page: 52 ident: b0545 article-title: The limits of privacy in automated profiling and data mining publication-title: Computer Law & Security Review – reference: . [22]. – reference: . [85]. – volume: 30 start-page: 381 year: 2011 end-page: 401 ident: b0410 article-title: Modeling managerial promotion decisions using Bayesian networks: An exploratory study publication-title: Journal of Management Development – reference: Pedreshi, D., Ruggieri, S., & Turini, F. (2008). Discrimination-aware data mining. In – reference: (pp. 275–278). – volume: 9 start-page: 31 year: 2008 end-page: 42 ident: b0255 article-title: Rough sets approach to human resource development of information technology corporations publication-title: International Journal of Simulation Systems, Science and Technology – reference: Wang, Z., Li, S., Wang, Y., & Li, S. (2009). The research of task assignment based on ant colony algorithm. In – reference: . [54]. – reference: (pp. 407–419). – reference: (pp. 2407–2413). [25]. – reference: Kao, W.-H., Huang, I.-M., Shen, H.-S., & Lin, C.-C. (2009). A study of weighted association rule applied in human resource job requirement. In – reference: Mohanraj, V., Lakshmipathi, R., Chandrasekaran, M., Senthilkumar, J., & Suresh, Y. (2009). Intelligent agent based talent evaluation engine using a knowledge base. In – volume: 38 start-page: 8220 year: 2011 end-page: 8228 ident: b0025 article-title: Classification by clustering decision tree-like classifier based on adjusted clusters publication-title: Expert Systems with Applications – reference: . [96]. – volume: 21 start-page: 677 year: 2007 end-page: 692 ident: b0070 article-title: The evolution of KDD: Towards domain-driven data mining publication-title: International Journal of Pattern Recognition and Artificial Intelligence – start-page: 33 year: 1984 end-page: 51 ident: b0135 article-title: A framework for strategic human resource management publication-title: Strategic human resource management – reference: (pp. 214–221). – year: 2005 ident: b0745 article-title: Information systems-supported personnel selection: An automated recommendation approach – reference: . [8] – year: 2005 ident: b0155 publication-title: Handbook of personnel selection – reference: . [53]. – volume: 23 start-page: 1888 year: 2012 end-page: 1907 ident: b0235 article-title: Employee behavioral options in problematic working conditions: Response pattern analysis publication-title: The International Journal of Human Resource Management – reference: (pp. 94–100). – volume: 13 start-page: 142 year: 2011 end-page: 173 ident: b0160 article-title: Prediction of arrival times and human resources allocation for container terminal publication-title: Maritime Economics and Logistics – reference: Dorn, J., & Hochmeister, M. (2009). Techscreen: Mining competencies in social software. In – reference: (pp. 198–203). [42]. – volume: 20 start-page: 123 year: 2003 end-page: 132 ident: b0110 article-title: Data mining for selection of insurance sales agents publication-title: Expert Systems with Applications – reference: Bal, M., Bal, Y., & Ustundag, A. (2011). Knowledge representation and discovery using formal concept analysis: An HRM application. In The World congress on engineering (pp. 1068–1073). [4]. – volume: 33 start-page: 149 year: 2003 end-page: 162 ident: b0435 article-title: Developing the profiles of truck drivers for their successful recruitment and retention: A data mining approach publication-title: International Journal of Physical Distribution & Logistics Management – volume: 3 start-page: 464 year: 2008 end-page: 481 ident: b0505 article-title: Data mining techniques for better decisions in human resource management systems publication-title: International Journal of Business Information Systems – reference: (pp. 1977–1980). – volume: 2007 start-page: 835 year: 2007 end-page: 838 ident: b0230 article-title: Application of fuzzy data mining in staff performance assessment publication-title: International Conference on Machine Learning and Cybernetics – reference: (pp. 82–85). – volume: Vol. 2774 start-page: 126 year: 2003 end-page: 410 ident: b0575 article-title: Data mining for seeking relationships between sickness absence and Japanese worker’s profile publication-title: Lecture notes in computer science – volume: 4 start-page: 67 year: 2011 end-page: 75 ident: b0020 article-title: Investigating the link between web data mining and strategic human resources planning publication-title: Computer and Information Science – reference: (pp. 44–49). – reference: (pp. 214–218). – volume: 17 start-page: 36 year: 1996 end-page: 53 ident: b0175 article-title: From data mining to knowledge discovery in databases publication-title: AI Magazine – reference: Khosla, R., & Goonesekera, T. (2003). An online multi-agent e-sales recruitment system. In – reference: Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In – volume: 59 start-page: 259 year: 2008 end-page: 265 ident: b0240 article-title: Simplifying essential competencies for Taiwan civil servants using the rough set approach publication-title: Journal of the Operational Research Society – reference: Liu, Y., Kou, Z., Perlich, C., & Lawrence, R. (2008). Intelligent system for workforce classification. In – reference: . [33]. – reference: Karahoca, A., Karahoca, D., & Kaya, O. (2008). Data mining to cluster human performance by using online self regulating clustering method. In – reference: Thissen-Roe, A. (2005). Adaptive selection of personality items to inform a neural network predicting job performance. Doctoral dissertation, University of Washington. Retrieved from – reference: (pp. 283–290). [86]. – volume: 47 start-page: 89 year: 2007 end-page: 104 ident: b0520 article-title: Integrating data mining and decision support through data mining based decision support system publication-title: Journal of Computer Information Systems – reference: (pp. 45–48). – volume: 36 start-page: 4114 year: 2009 end-page: 4124 ident: b0555 article-title: Framework for formal implementation of the business understanding phase of data mining projects publication-title: Expert Systems with Applications – year: 2003 ident: b0125 article-title: Data mining and modeling: So you have a model, now what? – reference: (pp. 160–163). – reference: (pp. 2334–2339). – reference: (pp. 324–327). – volume: 35 start-page: 513 year: 2005 end-page: 524 ident: b0440 article-title: Patent-based inventor profiles as a basis for human resource decisions in research and development publication-title: R&D Management – reference: (pp. 151–153). – reference: Färber, F., Weitzel, T., & Keim, T. (2003). An automated recommendation approach to selection in personnel recruitment. In – reference: (pp. 645–648). – reference: . [44]. – reference: (pp. 203–210). [1]. – reference: . [94]. – reference: (pp. 124–130). – reference: . [91]. – reference: Isaias, P., Casaca, C. & Pifano, S. (2010). Recommender systems for human resources task assignment. In – reference: Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In – volume: 20 start-page: 528 year: 2007 end-page: 541 ident: b0740 article-title: Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing publication-title: IEEE Transactions on Semiconductor Manufacturing – reference: Huang, Y. (2009). Study of college human resources data mining based on the SOM algorithm. In – reference: (pp. 349–351). – reference: Clyde, S., Zhang, J., & Yao, C.-C. (1995). An object-oriented implementation of an adaptive classification of job openings. In – reference: . [55]. – reference: . [97]. – reference: Han, J., Ren, J., & Dong, Y. (2008). Research on time series model for human resource management based on web technique. In – volume: 29 start-page: 783 year: 2005 end-page: 794 ident: b0595 article-title: Mining the generation Xers’ job attitudes by artificial neural network and decision tree – Empirical evidence in Taiwan publication-title: Expert Systems with Applications – reference: . [80]. – reference: . [79]. – volume: 38 start-page: 5129 year: 2011 end-page: 5132 ident: b0130 article-title: Improving sale performance prediction using support vector machines publication-title: Expert Systems with Applications – reference: (pp. 130–133). [6]. – reference: (pp. 744–750). – reference: (pp. 560–565). – reference: Li, X.-H., Zhao, J.-H., & Li, B.-H. (2004). Investigation of fuzzy mathematical model for general evaluation of administrators in university. In – reference: Sáiz, L., Pérez, A., Herrero, A., & Corchado, E. (2011). Analyzing key factors of human resources management. In – reference: . [41]. – start-page: 125 year: 2010 end-page: 141 ident: b0750 article-title: Privacy preserving data mining: How far can we go? publication-title: Data mining in private and public sectors: Organizational and governmental applications – volume: 83 start-page: 62 year: 2001 end-page: 64 ident: b0350 article-title: Data-mining technology lets retailers indentify which job candidates are likely to remain on the job publication-title: Stores Magazine – reference: . [71] – reference: . [65]. – reference: (pp. 115–126). [17]. – reference: (pp. 1–11). [56]. – reference: . [98]. – volume: 12 start-page: 237 year: 2008 end-page: 249 ident: b0475 article-title: Towards more relevance-oriented data mining research publication-title: Intelligent Data Analysis – volume: 19 start-page: 65 year: 2002 end-page: 85 ident: b0495 article-title: Building a model to predict caseworker and supervisor turnover using a neural network and logistic regression publication-title: Journal of Technology in Human Services – reference: Doctor, F., Hagras, H., Roberts, D., & Callaghan, V. (2009). A neuro-fuzzy based agent for group decision support in applicant ranking within human resources systems. In – reference: Beccera-Fernandez, I., & Rodriguez, J. (2001). Web data mining techniques for expertise-locator knowledge management systems. In – reference: . [89]. – reference: Weiss, G. M. (2009). Data mining in the real world: Experiences, challenges, and recommendations. In – reference: (pp. 2329–2339). [21]. – volume: Vol. 4819 start-page: 3 year: 2007 end-page: 14 ident: b0095 article-title: A survey of open source data mining systems publication-title: Lecture notes in computer science – reference: (pp. 411–416). – reference: . [84]. – reference: (pp. 11554–11557). – start-page: 53 year: 2010 end-page: 66 ident: b0640 article-title: Integrating SNA and DM technology into HR practice and research: Layoff prediction model publication-title: Studies in computational intelligence – reference: (pp. 945–948). – reference: Ye, J., Hu, H., & Chai, C. (2009). A talent classification method based on SVM. In – reference: (pp. 76–77). [46]. – volume: 2 start-page: 231 year: 2010 end-page: 236 ident: b0355 article-title: An intelligent agent based talent evaluation system, using a knowledge base publication-title: International Journal of Information Technology and Knowledge Management – volume: 23 start-page: 263 year: 2011 end-page: 289 ident: b0085 article-title: Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries publication-title: Flexible Services and Manufacturing Journal – reference: Bernik, M., & Bernik, I. (2011). Knowledge management and information technology in analyzing human resource processes. In – reference: . [99]. – ident: 10.1016/j.eswa.2012.10.059_b0480 doi: 10.1145/1401890.1401959 – volume: 5 start-page: 694 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0285 article-title: Knowledge discovery techniques for talent forecasting in human resource application publication-title: Journal of Human and Social Sciences – ident: 10.1016/j.eswa.2012.10.059_b0760 doi: 10.1109/JCPC.2009.5420070 – volume: 5 start-page: 186 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0570 article-title: Informating HRM: A comparison of data querying and data mining publication-title: International Journal of Business Information Systems doi: 10.1504/IJBIS.2010.030629 – ident: 10.1016/j.eswa.2012.10.059_b0580 doi: 10.2991/jcis.2006.46 – volume: 17 start-page: 36 issue: 3 year: 1996 ident: 10.1016/j.eswa.2012.10.059_b0175 article-title: From data mining to knowledge discovery in databases publication-title: AI Magazine – start-page: 1245 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0695 article-title: Commercial data mining software – volume: 26 start-page: 775 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0265 article-title: Knowledge discovery techniques for talent forecasting in human resource application publication-title: World Academy of Science, Engineering and Technology – volume: 16 start-page: 2020 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0450 article-title: Early prediction of employee attrition in software companies – Application of data mining techniques publication-title: Research and Practice in Human Resource Management – ident: 10.1016/j.eswa.2012.10.059_b0620 doi: 10.1109/NABIC.2009.5393596 – volume: 4 start-page: 23 issue: 5 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0560 article-title: Applicability of clustering and classification algorithms for recruitment data mining publication-title: International Journal of Computer Applications doi: 10.5120/823-1165 – ident: 10.1016/j.eswa.2012.10.059_b0140 doi: 10.1109/FUZZY.2009.5277379 – volume: 9 start-page: 31 issue: 2 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0255 article-title: Rough sets approach to human resource development of information technology corporations publication-title: International Journal of Simulation Systems, Science and Technology – ident: 10.1016/j.eswa.2012.10.059_b0445 doi: 10.1109/ARTCom.2009.214 – volume: 23 start-page: 263 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0085 article-title: Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries publication-title: Flexible Services and Manufacturing Journal doi: 10.1007/s10696-010-9068-x – start-page: 366 year: 2003 ident: 10.1016/j.eswa.2012.10.059_b0765 article-title: Data mining for human resource information systems – volume: 30 start-page: 381 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0410 article-title: Modeling managerial promotion decisions using Bayesian networks: An exploratory study publication-title: Journal of Management Development doi: 10.1108/02621711111126846 – volume: 29 start-page: 783 year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0595 article-title: Mining the generation Xers’ job attitudes by artificial neural network and decision tree – Empirical evidence in Taiwan publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2005.06.012 – ident: 10.1016/j.eswa.2012.10.059_b0390 doi: 10.1109/PACCS.2011.5990180 – ident: 10.1016/j.eswa.2012.10.059_b0625 doi: 10.1109/ICMA.2009.5246570 – volume: 50 start-page: 559 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0460 article-title: The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature publication-title: Decision Support Systems doi: 10.1016/j.dss.2010.08.006 – year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0745 – volume: 20 start-page: 123 year: 2003 ident: 10.1016/j.eswa.2012.10.059_b0110 article-title: Data mining for selection of insurance sales agents publication-title: Expert Systems with Applications doi: 10.1111/1468-0394.00235 – ident: 10.1016/j.eswa.2012.10.059_b0715 doi: 10.1109/FITME.2008.133 – volume: 13 start-page: 142 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0160 article-title: Prediction of arrival times and human resources allocation for container terminal publication-title: Maritime Economics and Logistics doi: 10.1057/mel.2011.3 – volume: 90 start-page: 395 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0615 article-title: 3DM: Domain-oriented data-driven data mining publication-title: Fundamenta Informaticae doi: 10.3233/FI-2009-0026 – volume: 35 start-page: 513 year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0440 article-title: Patent-based inventor profiles as a basis for human resource decisions in research and development publication-title: R&D Management doi: 10.1111/j.1467-9310.2005.00408.x – ident: 10.1016/j.eswa.2012.10.059_b0725 – volume: 3 start-page: 417 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0075 article-title: Employee turnover: A novel prediction solution with effective feature selection publication-title: WSEAS Transactions on Information Science and Applications – ident: 10.1016/j.eswa.2012.10.059_b0050 – ident: 10.1016/j.eswa.2012.10.059_b0565 – volume: Vol. 4827 start-page: 985 year: 2007 ident: 10.1016/j.eswa.2012.10.059_b0330 article-title: E-Gen: Automatic job offer processing system for human resources doi: 10.1007/978-3-540-76631-5_94 – volume: 47 start-page: 89 year: 2007 ident: 10.1016/j.eswa.2012.10.059_b0520 article-title: Integrating data mining and decision support through data mining based decision support system publication-title: Journal of Computer Information Systems – ident: 10.1016/j.eswa.2012.10.059_b0360 – start-page: 125 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0750 article-title: Privacy preserving data mining: How far can we go? – volume: 38 start-page: 8220 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0025 article-title: Classification by clustering decision tree-like classifier based on adjusted clusters publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.01.001 – volume: 19 start-page: 65 issue: 4 year: 2002 ident: 10.1016/j.eswa.2012.10.059_b0495 article-title: Building a model to predict caseworker and supervisor turnover using a neural network and logistic regression publication-title: Journal of Technology in Human Services doi: 10.1300/J017v19v04_05 – volume: 4 start-page: 67 issue: 3 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0020 article-title: Investigating the link between web data mining and strategic human resources planning publication-title: Computer and Information Science doi: 10.5539/cis.v4n3p67 – volume: 1 start-page: 3 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0030 article-title: The state of educational data mining in 2009: A review and future visions publication-title: Journal of Educational Data Mining – ident: 10.1016/j.eswa.2012.10.059_b0170 – volume: 32 start-page: 2635 year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0550 article-title: Employee turnover: A neural network solution publication-title: Computers and Operations Research doi: 10.1016/j.cor.2004.06.022 – volume: 38 start-page: 5129 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0130 article-title: Improving sale performance prediction using support vector machines publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.10.003 – ident: 10.1016/j.eswa.2012.10.059_b0395 – volume: Vol. 3865 start-page: 365 year: 2006 ident: 10.1016/j.eswa.2012.10.059_b0515 article-title: Recommendation for team and virtual community formations based on competence mining doi: 10.1007/11686699_37 – ident: 10.1016/j.eswa.2012.10.059_b0585 – ident: 10.1016/j.eswa.2012.10.059_b0015 doi: 10.1109/CIDM.2009.4938650 – ident: 10.1016/j.eswa.2012.10.059_b0405 – volume: 5 start-page: 1 year: 2001 ident: 10.1016/j.eswa.2012.10.059_b0500 article-title: GPR: A data mining tool using genetic programming publication-title: Communications of the Association for Information Systems doi: 10.17705/1CAIS.00506 – volume: 1 start-page: 108 issue: 4 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0290 article-title: Decision support system in human resource management (a study of HR intelligent techniques) publication-title: International Journal of Research in IT, Management and Engineering – ident: 10.1016/j.eswa.2012.10.059_b0770 doi: 10.3233/978-1-60750-633-1-49 – ident: 10.1016/j.eswa.2012.10.059_b0340 doi: 10.1109/CIMSA.2006.250749 – ident: 10.1016/j.eswa.2012.10.059_b0690 doi: 10.1109/PEITS.2009.5407033 – volume: 37 start-page: 434 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0365 article-title: Exploring high-performers’ required competencies publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2009.05.064 – start-page: 431 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0325 article-title: Job offer management: How improve the ranking of candidates – volume: Vol. 3812 start-page: 177 year: 2006 ident: 10.1016/j.eswa.2012.10.059_b0425 article-title: Mining staff assignment rules from event-based data doi: 10.1007/11678564_16 – start-page: 33 year: 1984 ident: 10.1016/j.eswa.2012.10.059_b0135 article-title: A framework for strategic human resource management – volume: 23 start-page: 1888 year: 2012 ident: 10.1016/j.eswa.2012.10.059_b0235 article-title: Employee behavioral options in problematic working conditions: Response pattern analysis publication-title: The International Journal of Human Resource Management doi: 10.1080/09585192.2011.610340 – ident: 10.1016/j.eswa.2012.10.059_b0250 doi: 10.1109/PICMET.2008.4599705 – volume: Vol. 6064 start-page: 532 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0190 article-title: A neural network based model for project risk and talent management doi: 10.1007/978-3-642-13318-3_66 – start-page: 375 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0530 article-title: The deployment of data mining into operational business processes – volume: 22 start-page: 232 year: 2004 ident: 10.1016/j.eswa.2012.10.059_b0605 article-title: Predicting nurses’ intention to quit with a support vector machine: A new approach to set up an early warning mechanism in human resource management publication-title: Computers, Informatics, Nursing doi: 10.1097/00024665-200407000-00012 – ident: 10.1016/j.eswa.2012.10.059_b0245 doi: 10.1109/APCIP.2009.89 – year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0315 – volume: 33 start-page: 149 year: 2003 ident: 10.1016/j.eswa.2012.10.059_b0435 article-title: Developing the profiles of truck drivers for their successful recruitment and retention: A data mining approach publication-title: International Journal of Physical Distribution & Logistics Management doi: 10.1108/09600030310469153 – ident: 10.1016/j.eswa.2012.10.059_b0180 doi: 10.1109/ISISE.2009.55 – ident: 10.1016/j.eswa.2012.10.059_b0045 – ident: 10.1016/j.eswa.2012.10.059_b0345 – ident: 10.1016/j.eswa.2012.10.059_b0775 doi: 10.1109/CSCWD.2005.194143 – volume: 21 start-page: 677 year: 2007 ident: 10.1016/j.eswa.2012.10.059_b0070 article-title: The evolution of KDD: Towards domain-driven data mining publication-title: International Journal of Pattern Recognition and Artificial Intelligence doi: 10.1142/S0218001407005612 – ident: 10.1016/j.eswa.2012.10.059_b0205 doi: 10.1109/WiCom.2008.2687 – volume: Vol. 6276 start-page: 491 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0275 article-title: Classification and prediction of academic talent using data mining techniques doi: 10.1007/978-3-642-15387-7_53 – volume: 22 start-page: 206 year: 2006 ident: 10.1016/j.eswa.2012.10.059_b0485 article-title: EU data protection policy. The directive 95/46/EC: Ten years after publication-title: Computer Law and Security Review doi: 10.1016/j.clsr.2006.03.004 – volume: 36 start-page: 4114 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0555 article-title: Framework for formal implementation of the business understanding phase of data mining projects publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.03.021 – ident: 10.1016/j.eswa.2012.10.059_b0200 – volume: 83 start-page: 62 year: 2001 ident: 10.1016/j.eswa.2012.10.059_b0350 article-title: Data-mining technology lets retailers indentify which job candidates are likely to remain on the job publication-title: Stores Magazine – volume: 59 start-page: 259 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0240 article-title: Simplifying essential competencies for Taiwan civil servants using the rough set approach publication-title: Journal of the Operational Research Society doi: 10.1057/palgrave.jors.2602516 – ident: 10.1016/j.eswa.2012.10.059_b0040 – ident: 10.1016/j.eswa.2012.10.059_b0710 doi: 10.1109/IITA.Workshops.2008.235 – volume: 37 start-page: 2427 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0600 article-title: A rule-based CBR approach for expert finding and problem diagnosis publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2009.07.037 – ident: 10.1016/j.eswa.2012.10.059_b0630 – ident: 10.1016/j.eswa.2012.10.059_b0755 doi: 10.1109/IFCSTA.2009.90 – ident: 10.1016/j.eswa.2012.10.059_b0370 – volume: 34 start-page: 280 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0105 article-title: Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2006.09.003 – volume: 38 start-page: 1999 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0540 article-title: Employee churn prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.07.134 – ident: 10.1016/j.eswa.2012.10.059_b0730 – ident: 10.1016/j.eswa.2012.10.059_b0300 – year: 1999 ident: 10.1016/j.eswa.2012.10.059_b0210 – ident: 10.1016/j.eswa.2012.10.059_b0010 doi: 10.1109/ICDE.1995.380415 – ident: 10.1016/j.eswa.2012.10.059_b0780 doi: 10.1109/ISBIM.2008.187 – volume: Vol. 3642 start-page: 484 year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0650 article-title: Simplifying the manager competency model by using the rough set approach doi: 10.1007/11548706_51 – volume: 36 start-page: 9574 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0645 article-title: Exploring core competencies for R&D technical professionals publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.07.052 – ident: 10.1016/j.eswa.2012.10.059_b0535 doi: 10.1007/978-3-642-23878-9_55 – volume: 20 start-page: 501 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0115 article-title: Data mining in manufacturing: A review based on the kind of knowledge publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-008-0145-x – volume: Vol. 4819 start-page: 3 year: 2007 ident: 10.1016/j.eswa.2012.10.059_b0095 article-title: A survey of open source data mining systems doi: 10.1007/978-3-540-77018-3_2 – ident: 10.1016/j.eswa.2012.10.059_b0380 doi: 10.1109/ICICTA.2009.235 – volume: 8 start-page: 313 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0430 article-title: Assessing the prediction of employee productivity: A comparison of OLS vs. CART publication-title: International Journal of Productivity and Quality Management doi: 10.1504/IJPQM.2011.042511 – volume: 3 start-page: 464 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0505 article-title: Data mining techniques for better decisions in human resource management systems publication-title: International Journal of Business Information Systems doi: 10.1504/IJBIS.2008.018597 – volume: 40 start-page: 601 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0525 article-title: Educational data mining: A review of the state of the art publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews doi: 10.1109/TSMCC.2010.2053532 – year: 2003 ident: 10.1016/j.eswa.2012.10.059_b0125 – volume: 7 start-page: 41 issue: 4 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0005 article-title: Domain driven data mining – Application to business publication-title: International Journal of Computer Science Issues – volume: Vol. 3610 start-page: 668 year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0220 article-title: Application of support vector machines in predicting employee turnover based on job performance doi: 10.1007/11539087_85 – ident: 10.1016/j.eswa.2012.10.059_b0700 doi: 10.1109/ISCID.2009.220 – ident: 10.1016/j.eswa.2012.10.059_b0270 doi: 10.1109/ISDA.2009.64 – ident: 10.1016/j.eswa.2012.10.059_b0385 doi: 10.1109/ICMLC.2004.1382104 – start-page: 219 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0375 article-title: Research on talent introduction hazard and training strategy of university based on data mining – start-page: 223 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0735 article-title: Data warehouse and business intelligence systems in the context of e-HRM – volume: 2 start-page: 2526 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0280 article-title: Human talent prediction in HRM using c4.5 classification algorithm publication-title: International Journal on Computer Science and Engineering – volume: 12 start-page: 237 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0475 article-title: Towards more relevance-oriented data mining research publication-title: Intelligent Data Analysis doi: 10.3233/IDA-2008-12206 – ident: 10.1016/j.eswa.2012.10.059_b0055 – ident: 10.1016/j.eswa.2012.10.059_b0145 – volume: Vol. 5333 start-page: 625 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0320 article-title: Automatic profiling system for ranking candidates answers in human resources doi: 10.1007/978-3-540-88875-8_86 – ident: 10.1016/j.eswa.2012.10.059_b0420 – volume: 16 start-page: 81 year: 2008 ident: 10.1016/j.eswa.2012.10.059_b0455 article-title: Establishing a link between employee turnover and withdrawal behaviours: Application of data mining techniques publication-title: Research and Practice in Human Resource Management – ident: 10.1016/j.eswa.2012.10.059_b0720 doi: 10.1109/WI.2005.99 – volume: 26 start-page: 217 year: 2004 ident: 10.1016/j.eswa.2012.10.059_b0150 article-title: An expert system for job matching of the unemployed publication-title: Expert Systems with Applications doi: 10.1016/S0957-4174(03)00136-2 – ident: 10.1016/j.eswa.2012.10.059_b0080 doi: 10.1109/ASONAM.2009.52 – volume: 36 start-page: 2592 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0465 article-title: Application of data mining techniques in customer relationship management: A literature review and classification publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.02.021 – volume: 22 start-page: 755 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0065 article-title: Domain-driven data mining: Challenges and prospects publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2010.32 – ident: 10.1016/j.eswa.2012.10.059_b0225 – ident: 10.1016/j.eswa.2012.10.059_b0675 doi: 10.1109/ICEOE.2011.6013123 – ident: 10.1016/j.eswa.2012.10.059_b0400 doi: 10.1109/BICTA.2009.5338100 – ident: 10.1016/j.eswa.2012.10.059_b0590 – ident: 10.1016/j.eswa.2012.10.059_b0090 doi: 10.1109/ICIII.2010.200 – volume: 36 start-page: 3378 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0660 article-title: Improving scheduling of emergency physicians using data mining analysis publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.02.069 – ident: 10.1016/j.eswa.2012.10.059_b0335 doi: 10.1109/WI.2003.1241181 – volume: 20 start-page: 528 year: 2007 ident: 10.1016/j.eswa.2012.10.059_b0740 article-title: Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing publication-title: IEEE Transactions on Semiconductor Manufacturing doi: 10.1109/TSM.2007.907630 – volume: 6 start-page: 139 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0685 article-title: The data mining of the human resources data warehouse in university based on association rule publication-title: Journal of Computers doi: 10.4304/jcp.6.1.139-146 – ident: 10.1016/j.eswa.2012.10.059_b0655 doi: 10.1109/ICICISYS.2010.5658634 – ident: 10.1016/j.eswa.2012.10.059_b0260 doi: 10.1109/AINA.2010.168 – ident: 10.1016/j.eswa.2012.10.059_b0310 – ident: 10.1016/j.eswa.2012.10.059_b0470 doi: 10.1109/EBISS.2010.5473417 – volume: 27 start-page: 45 year: 2011 ident: 10.1016/j.eswa.2012.10.059_b0545 article-title: The limits of privacy in automated profiling and data mining publication-title: Computer Law & Security Review doi: 10.1016/j.clsr.2010.11.009 – ident: 10.1016/j.eswa.2012.10.059_b0610 – start-page: 53 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0640 article-title: Integrating SNA and DM technology into HR practice and research: Layoff prediction model doi: 10.1007/978-3-642-13422-7_4 – ident: 10.1016/j.eswa.2012.10.059_b0665 doi: 10.1109/ICMLC.2008.4620484 – ident: 10.1016/j.eswa.2012.10.059_b0635 doi: 10.1145/1571941.1571991 – ident: 10.1016/j.eswa.2012.10.059_b0120 doi: 10.1109/CAIA.1995.378795 – year: 2005 ident: 10.1016/j.eswa.2012.10.059_b0155 – volume: 2007 start-page: 835 year: 2007 ident: 10.1016/j.eswa.2012.10.059_b0230 article-title: Application of fuzzy data mining in staff performance assessment publication-title: International Conference on Machine Learning and Cybernetics – start-page: 197 year: 2009 ident: 10.1016/j.eswa.2012.10.059_b0295 article-title: Early warning model research of state-owned enterprises’ human capital risks based on improved neural network publication-title: International Conference on Future BioMedical Information Engineering – volume: Vol. 2774 start-page: 126 year: 2003 ident: 10.1016/j.eswa.2012.10.059_b0575 article-title: Data mining for seeking relationships between sickness absence and Japanese worker’s profile doi: 10.1007/978-3-540-45226-3_56 – ident: 10.1016/j.eswa.2012.10.059_b0785 doi: 10.1109/FSKD.2010.5569250 – ident: 10.1016/j.eswa.2012.10.059_b0215 doi: 10.1109/ICSMC.2005.1571205 – ident: 10.1016/j.eswa.2012.10.059_b0670 doi: 10.1109/IUCE.2009.63 – volume: 2 start-page: 231 year: 2010 ident: 10.1016/j.eswa.2012.10.059_b0355 article-title: An intelligent agent based talent evaluation system, using a knowledge base publication-title: International Journal of Information Technology and Knowledge Management |
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| Snippet | ► We review data mining in human resource management as a new and prospering field of research using a domain driven framework. ► Findings show a large number... An increasing number of publications concerning data mining in the subject of human resource management (HRM) indicate the presence of a prospering new... |
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| SubjectTerms | Applied sciences Computer science; control theory; systems Data mining Data processing. List processing. Character string processing Documents Domain driven data mining Electronic human resource management (e-HRM) Exact sciences and technology Expert systems Firm modelling HR data mining HRM Human resource management Legal Literature review Memory organisation. Data processing Operational research and scientific management Operational research. Management science Recognition Software |
| Title | Domain driven data mining in human resource management: A review of current research |
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