In this thesis, radial basis function neural networks based on incremental fuzzy clustering are designed for big data processing. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered as the activation function of radial basis function neural networks, which could effectively do big data processing. There are two incremental clustering techniques such as single data incremental type and group data incremental type. These techniques depend on the processing method of data. In the conclusion phase, the connection weights of networks are given as the coefficients of linear polynomial function which is the extended type of constant term. And then the connection weights are calculated by the least square estimation-based learning. There are two estimation methods such as recursive least square estimation and block least square estimation, which could effectively do big data processing. The recursive least square estimation and the block least square estimation depend on the processing method of data like as the incremental fuzzy clustering techniques indicated previously. In the inference phase, a final output is obtained by fuzzy inference method. Some machine learning datasets are considered to evaluate the performance of the proposed model as well as pattern classifier, and the experiments are carried out for two methods such as modeling and pattern classification. Root mean square error and pattern classification rate are used as the performance index of modeling as well as pattern classification. From the experiments results, the proposed model and pattern classifier are compared with other previous studies and also analyzed.