K-means method for rough classification of R&D employees' performance evaluation

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.