Synthetic Rating on Talent Evaluation-Similarity of Subsets

There are many topics about rating individuals, animals, places, things, or abstract ideas that are actively researched. When rating about an object is needed to form an opinion it is often given by an expert in the field. These ratings vary from one individual expert's rating to another due to the subjective nature of the evaluation process. How can we evaluate the ratings? How can we find the correlations and similarities among these sets? How can we provide a mathematical modelling for a rating problem? This paper provides a procedure for the extension of fuzzy synthetic rating modelling on a sample to the entire data set and introduces a k-means clustering method to check the level to which there exist similarities among the subsets and classify the dataset automatically for a rating problem. The related synthetic rating and an example to illustrate the modelling is given in this work.