Talent knowledge acquisition using data mining classification techniques

Data Mining classification task is categorized as a part of knowledge acquisition process, which can be implemented through the analysis procedure in related databases. In this study, we aimed to employ this technique to perform talent knowledge acquisition process in Human Resource (HR) by using talent databases. In HR, among the challenges of HR professionals is to manage organization's talents, especially to ensure the right person assign to the right job at the right time. In this case, knowledge discovered from talent knowledge acquisition process can be used by professionals in HR to handle various tasks in talent management. In this article, we present an experimental study to identify the potential data mining classification technique for talent knowledge acquisition. Talent knowledge discovered from related databases can be used to classify the appropriate talent among employees. In experimental phase, we used selected classification algorithms in order to propose the suitable classifier from talent datasets. As a result, the C4.5 classifier algorithm from decision tree family is recommended as a suitable classifier for the datasets. Classification model performed by this classifier can be used in talent management especially for talent classification or prediction.