Classification algorithms for knowledge mapping of expert in Energy industry

Data classification is one technique of data mining using for creating knowledge map. The knowledge map describes who has what tacit knowledge and where the knowledge is collected. Knowledge map further helps employees to learn the jobs and expertise in organization. In many organizations there is a lack of guidelines for knowledge management and knowledge mapping. The objectives of this paper is to propose a classification algorithm for creating knowledge map of experts in an organization. The data set using in this paper are in the domain of energy experts. The performances of four classification algorithms are measured by comparing their prediction powers on expert classification. Four candidate algorithms include two types of Decision Trees (ID3 and C4.5), and two Rule-based (OneR and Prism). Results show that C4.5 algorithm is the best one, while ID3 is the last one in classification for knowledge mapping. Future research and implication are also suggested.

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