Name
Title | ||
|---|---|---|
An empirical analysis on auto corporation training program planning by data mining techniques |
Abstract | ||
|---|---|---|
Under limited resources in corporation education training, to enhance human resources quality, making education training program planning more efficient is a significant issue in training future talents. In accordance with Taiwan TrainQuali System (TTQS), the basic training structure is ton specify P (Plan) and D (Design). Ensuing results will be easier and successful. From TTQS database of Bureau of Employment and Vocational Training, corporations in Taoyuan, Hsinchu and Miaoli winning Gold Medals (Group B) have gaps outside control line in P and D. Enhancement is needed in the gap. The paper aims at a certain company winning Gold Medals in Taoyuan, Hsinchu and Miaoli to locate hidden or unobvious information with data mining, which will help future education training course planning and design. The researchers use two-stage clustering (SOM and K-means) under data mining theory to collect personnel training data of Automobile Corporation A in Taiwan and China with data mining and analysis. The results under the two algorithms will serve as reference for future education training courses. In the end, in combination of back-propagation neural network to develop education training prediction model, the research offers reference for writing knowledge management system to enhance effects of personnel participation in training at corporations. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2010.11.038 | Expert Syst. Appl. |
Keywords | Field | DocType |
data mining,human resources,back-propagation artificial neural network,education training program planning,empirical analysis,gold medals,personnel training data,corporation education training,data mining theory,basic training structure,ttqs (taiwan trainquali system),data mining technique,k-means,auto corporation training program,som (self-organization map),future education training course,education training prediction model,future talent,k means,vocational training,back propagation,human resource,prediction model,artificial neural network,knowledge management system | Data mining,Human resources,Computer science,Knowledge management,Training program,Artificial intelligence,Cluster analysis,Training set,Corporation,k-means clustering,Vocational education,Back propagation artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 5 | Expert Systems With Applications |
Citations | PageRank | References |
1 | 0.36 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| W. T. Lin | 1 | 1 | 0.36 |
| S. J. Wang | 2 | 1 | 0.36 |
| Yik-Chung Wu | 3 | 1335 | 94.03 |
| Tianchun Ye | 4 | 30 | 8.32 |