In Taiwan, most industries are of small and medium scale, and there are limited resources for educational training. Increasing the quality of personnel by cultivating talents for the future becomes an extremely important issue. With the growth of firms and the increase in their needs, the database is also growing. We should therefore determine how to recognize and extract the useful information contained in this database in order to apply it in such a way that assists companies in meeting their increasing and changing needs. This research collects data of personnel educational training in China Motor Corporation by cluster analysis, decision tree algorithm and back-propagation neural networks for mining analysis and classification. Based on the algorithm classification result, we finally propose the demand model suitable for educational training in other related industries. The research is expected to explore how to maximize results through planning the courses and the personnel's participation in the training. We try to determine the key factors essential to the success of educational training. Once identified, this information can then serve as the basis for other firms' future planning of educational training strategies with regard to innovation and breakthrough.
['Chiao-Tzu Huang', 'Wen-Tsann Lin', 'Shen-Tsu Wang', 'Wen-Shan Wang']
Planning of educational training courses by data mining: Using China Motor Corporation as an example