International Journal of Computer Applications
The task to capture and interpret information hidden inside high-dimensional data can be considered very complicated and challenging. Usually, dimension reduction technique may be considered as the first step to data analysis and exploration. The focus of this paper is on high-dimensional data dimension reduction using a supervised artificial neural networks technique known as Auto-Associative Neural Networks (AANN). The AANN can be considered as a powerful tool in data analysis and clustering with the ability to deal with linear and nonlinear correlation among variables.