Applying Fuzzy Clustering Algorithm and Knowledge Structure Approach on Professional Certification on Nurses

This study is to provide an integrated method and algorithm for knowledge structure analysis and cognition diagnosis. Cooperating fuzzy clustering and graphic structures analysis, that features of knowledge structures of each cluster are clearly displayed. Furthermore, fuzzy clustering is used to classify examinee based on response pattern of testing data. The results show that students of varied cluster own distinct knowledge structures. Combined with fuzzy clustering algorithm based on normalized Mahalanobis distance could be very feasible for cognition diagnosis. Based on the findings and results, it shows that knowledge structures will be feasible for remedial instruction and helps students get more chance to get professional certification on nurses.