Abstract
This research applies clustering analysis technology of data mining to predict trend of the Technology Professionals turnover rate, including SOM (Self-Organizing Map) combined with Artificial Neural Network clustering analysis method. Meanwhile, this hybrid clustering method is applied to research the individual characteristics of turnover trend clusters. The turnover high peak period which is after Chinese calendar and an age bracket of high alteration circle has been consider for major research target and also used to be the transaction questionnaire. All Technology Professionals’ case has been attached in Taiwan famous company. According to our research, the results show the high outstanding turnover trend circle mainly caused by non- identification of inner fidelity identification, leadership and management. The clustering accuracy rate reaches 92.7% by way of cross-verification. The application of this model, also helps rapidly prevent the problem for loss of key human-resource. Meanwhile, this will excite the organization to learn to enhance the enterprise competition ability and improve the efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dalton, D.R., Todor, W.D., Krackhardt, D.M.: Turnover Overstated: The Functional Taxonomy. Academy of Management Review 7, 118–119 (1982)
Newman, J.E.: Predicting Absenteeism and Turnover: A Field Comparison of Fishbein’s Mold and Traditional Job Attitude Measures. Journal of Applied Psychology 59, 610–615 (1974)
Kraut, A.I.: Predicting Turnover of Employees form Measured Job Attitudes. Organizational Behavior and Human Performance 13, 233–243 (1975)
Mobley, W.H.: Intermediate Linkages in the Relationship between Job Satisfaction and Employee Turnover. Journal of Applied Psychology 62(2), 237–240 (1977)
Miller, H.E., Katerberg, H.C.L.: Evaluation of the Mobley, Horner and Hollingsworth Model of Employee Turnover. Journal of Applied Psychology 64, 509–517 (1979)
Michaels, C.E., Spector, P.E.: Causes of Employee Turnover: A Test of The Mobley, Griffrth, Hand, and Meglino Model. Journal of Applied Psychology 67(1), 53–59 (1982)
Porter, L.W., Steers, R.M.: Organizational, work, and Personal factors in Employee Turnover and Absenteeism. Psychological Bulletin 80(2), 151–176 (1973)
Chen, H., Roussinov, D.G.: Document Clustering for Electronic Meetings: an Experimental Comparison of Two Techniques. Decision Support Systems 27(1-2), 67–79 (1999)
Chang, P.C., Liu, C.H.: A TSK Type Fuzzy Rule based System for Stock Price Prediction. Expert Systems with Applications 34(1), 135–144 (2008)
Chang, P.C., Fan, C.Y., Wang, Y.W.: Evolving CBR and Data Segmentation by SOM for Flow Time Prediction in Semiconductor Manufacturing Factory. Journal of Intelligent Manufacturing
Chang, P.C., Liao, T.W.: Combining SOM and Fuzzy Rule Base for Flow Time Prediction in Semiconductor Manufacturing Factory. Applied Soft Computing Journal 6(2), 198–206 (2006)
Sgarma, S.: Applied Multivariate Techniques, pp. 212–216. Wiley, Chichester (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, C.S., Fan, CY., Fan, PS., Wang, YW. (2010). Hybrid Self-Organizing Map and Neural Network Clustering Analysis for Technology Professionals Turnover Rate Forecasting. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-14831-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
eBook Packages: Computer ScienceComputer Science (R0)