K-means method for rough classification of R&D employees' performance evaluation
Hong Tau Lee, Sheu Hua Chen, Jie Lin · International Transactions in Operational Research · 2006
This paper proposes an approach that can roughly cluster a data set with fuzzy linguistic entries as a prior data arrangement for performance evaluation of R&D employees. The extension principles of fuzzy linguistic numbers are used to modify the K-means method for handling the linguistic data set. We define the absolute difference of fuzzy linguistic variables as their fuzzy distance. Based on this definition, the K-means approach can be modified slightly for clustering purposes. The performance of employees engaged in designing and R&D-oriented jobs is possibly related to some qualitative attributes and the evaluation of such attributes for each employee has a tendency toward semantic scales. In the proposed approach, the supervisor can evaluate the performance of each employee directly with a semantic scale. The modified K-means approach can roughly cluster their performance into different classes in advance of applying some other sophisticated processes.