Paper Info
Title
Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals
Abstract
This study applies clustering analysis for data mining and machine learning to predict trends in technology professional turnover rates, including the hybrid artificial neural network and clustering analysis known as the self-organizing map (SOM). This hybrid clustering method was used to study the individual characteristics of turnover trend clusters. Using a transaction questionnaire, we studied the period of peak turnover, which occurs after the Chinese New Year, for individuals divided into various age groups. The turnover trend of technology professionals was examined in well-known Taiwanese companies. The results indicate that the high outstanding turnover trend circle was primarily caused by a lack of inner fidelity identification, leadership and management. Based on cross-verification, the clustering accuracy rate was 92.7%. This study addressed problems related to the rapid loss of key human resources and should help organizations learn how to enhance competitiveness and efficiency.
Year
DOI
Venue
2012
10.1016/j.eswa.2012.02.005
Expert Syst. Appl.
Keywords
Field
DocType
turnover trend cluster,technology professional,hybrid artificial neural network,hybrid clustering method,clustering analysis,technology professional turnover rate,high outstanding turnover trend,clustering accuracy rate,hybrid data mining,peak turnover,turnover trend,self organizing map
Data science,Turnover,Fidelity,Human resources,Computer science,Self-organizing map,Hybrid data,Artificial intelligence,Artificial neural network,Database transaction,Cluster analysis,Machine learning
Journal
Volume
Issue
ISSN
39
10
0957-4174
Citations 
PageRank 
References 
3
0.47
1
Authors
4
Name
Order
Citations
PageRank
Chin-Yuan Fan147328.27
Fan Pei-Shu242.20
Teyi Chan3759.33
Shu-Hao Chang4143.04