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The need to handle uncertainty and vagueness in real world becomes a necessity for developing good and efficient systems. Fuzzy rules and their usage in fuzzy systems help too much in solving these problems away from the complications of probability mathematical calculations. Fuzzy rules deals will words and labels instead of values of the variables. These labels are called variable's subsets and needed to be prepared carefully to make sure that the fuzzy rules depend on accurate propositions. This research tries to design an efficient set of rules that is used later for inference by a hybrid model of Self Organized Features Maps and Parallel Genetic Algorithms. Self Organized Features Maps capabilities to cluster inputs using self adoption techniques have been very useful in generating fuzzy membership functions for the subsets of the fuzzy variables. Then the Parallel Genetic Algorithms use these membership functions along with the training data set to find the most fit fuzzy rule set from a number of initial sub populations according to the fitness function. The illustrations of the proposed model and its sub modules along with the experimental results and comparisons with previous techniques in generating rules from data sets are declared.

Abstract-Because of the patient's inconsistent data, uncertain Thyroid Disease dataset is appeared in the learning process: irrelevant, redundant, missing, and huge features. In this paper, Rough sets theory is used in data discretization for continuous attribute values, data reduction and rule induction. Also, Rough sets try to cluster the Thyroid relation attributes in the presence of missing attribute values and build the Modified Similarity Relation that is dependent on the number of missing values with respect to the number of the whole defined attributes for each rule. The discernibility matrix has been constructed to compute the minimal sets of reducts, which is used to extract the minimal sets of decision rules that describe similarity relations among rules. Thus, the rule associated strength is measured.