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In fuzzy neural network systems, fuzzy membership functions play a key role in making the fuzzy sets organize the input data knowledge in an appropriate and representative manner. Earlier clustering techniques exploit some uniform, convex algebraic functions, such as Gaussian, triangular or trapezoidal to represent the fuzzy sets. However, due to the irregularity of the input data, regular and uniform fuzzy sets may not be able to represent the exact feature information of input data. In order to address this issue, a clustering method called modified discrete clustering technique (MDCT) is proposed in this paper. MDCT represents non-uniform, and normal fuzzy sets with a set of irregular sampling points. The sampling points learn the knowledge of data feature in an irregular and flexible manner. Thus, the fuzzy membership functions generated using these sampling points can provide a better representation of the actual input data.