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Examines several clustering methods for structure learning in constructing efficient neuro-fuzzy systems. The structure learning establishes the internal structure (i.e., the number of term sets and fuzzy-rule base generation) of a given neuro-fuzzy architecture. The fundamental ideas of existing rule generation algorithms are addressed and discussed. Performance of the neuro-fuzzy systems established from these clustering methods is validated through computer simulations of the classification problem of IRIS and the control example of an autonomous underwater vehicle.