Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
A similarity-based generalization of fuzzy orderings preserving the classical axioms
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Introduction to Systems Science
An Introduction to Systems Science
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The main purpose of this study is to provide an integrated method and algorithm for knowledge structure analysis and cognition diagnosis. Fuzzy clustering and algorithm of graphic structures analysis are combined so that features of knowledge structures of each cluster are clearly displayed. Concept structure analysis (CSA) could provide individualized knowledge structure. CSA algorithm is the major methodology and it is based on fuzzy logic model of perception (FLMP) and interpretive structural modeling (ISM). CSA could display individualized knowledge structure and clearly represent hierarchies and linkage among concepts for each examinee. Furthermore, fuzzy clustering is used to classify examinee based on response pattern of testing data. Therefore, CSA will be more effectively to display features of each cluster. In this study, the author provide the empirical data for concepts of linear algebra from university students. The results show that students of varied cluster own distinct knowledge structures. CSA combined with fuzzy clustering could be very feasible for cognition diagnosis. Based on the findings and results, some suggestions and recommendations for future research are provided.