Data mining based on CMAC neural networks

Neural Networks are a widely used Data Mining technique, but for high-dimensional datasets, the training process of the normal neural networks, such as multilyer perceptron (MLP), is very slow. It is an important drawback for using them in real-time Data Mining applications where the main requirement is to have an answer within a short time. In this research work we propose a CMAC Neural Network adaptation for Data Mining, which most provides fast training time and guaranteed convergence. This paper describes how we built a CMAC adaptation for Data Mining, obtaining a classification model that can be applied to real-life datasets. Experimental results show that CMAC may be an alternative model for high-dimensional data classification in Data Mining.