Clustering algorithms have an important role in exploring data structure via grouping similar data points or objects into a subset called cluster. Numerous clustering algorithms have been proposed, but almost of them need a good initialization, data structural dependent, or require huge computational time. In this paper, we propose a novel clustering algorithm, called Cooperative and Iterative Evaluation Exchange (Co-IEE). The proposed algorithm considers all data points as potential cluster centers, and calculates, then exchanges real-valued evaluations to find the optimal clustering result of data set. The proposed Co-IEE has been applied into problem of Vietnamese university classification, achieving state-of-the-art results from both clustering evaluation, as well as human observation.