Article ID: | iaor20072789 |
Country: | United Kingdom |
Volume: | 13 |
Issue: | 4 |
Start Page Number: | 365 |
End Page Number: | 377 |
Publication Date: | Jul 2006 |
Journal: | International Transactions in Operational Research |
Authors: | Lee Hong Tau, Chen Sheu Hua |
Keywords: | fuzzy sets |
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.