In this paper, we explore the applicability of K-means and Fuzzy C-Means
clustering algorithms to student allocation problem that allocates new students
to homogenous groups of specified maximum capacity, and analyze effects of such
allocations on the academic performance of students. The paper also presents a
Fuzzy set and Regression analysis based Dynamic Fuzzy Expert System model which
is capable of dealing with imprecision and missing data that is commonly
inherited in the student academic performance evaluation. This model automatically
converts crisp sets into fuzzy sets by using C-Means clustering algorithm
method. The comparative performance analysis indicates that the student group
formed by Fuzzy C-Means clustering algorithm performed better than groups
formed by K-Means, classical fuzzy logic clustering algorithms and Bayesian
classifications.
Cite this paper
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