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• W2086175064 abstract "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." @default.
• W2086175064 created "2016-06-24" @default.
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• W2086175064 date "2006-07-01" @default.
• W2086175064 modified "2023-10-04" @default.
• W2086175064 title "K-means method for rough classification of R&D employees' performance evaluation" @default.
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• W2086175064 doi "https://doi.org/10.1111/j.1475-3995.2006.00553_t.x" @default.
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