It is very difficult to determine the required number of objects with traditional methods if most of the total scores are the same or most of the alternative objects are subject to the same class. The purpose of this paper is to develop a grey clustering method with center–point triangular possibility functions considering dynamic preferences for this kind of evaluation.
First, the problems existing in the present possibility functions are discussed in detail. Then center–point triangular possibility functions with dynamic preferences are provided. Considering the correlation and contrast between indicators, criteria importance, though intercrieria correlation, is extended with grey forms, which determine the weights of indicators. Then, a new grey clustering method is proposed. Finally, the proposed method is illustrated in evaluating online curriculums.
Our research shows that the results of clustering are related to dynamic preferences. The number of objects subject to the same class decreases with dynamic preferences. We can select the best among the best via adjusting dynamic preferences.
It is the first time to integrate dynamic preferences into center–point triangular possibility functions. The proposed method provides an effective solution for evaluations to select the best among the best. This method can also be applied in other evaluation cases under similar conditions.
