Dimensionality Reduction in Data Mining Using Artificial Neural Networks

Abstract

The use of classic dimension reduction techniques can be considered customary practice within the context of data mining (DM). Nevertheless, although artificial neural networks (ANNs) are one of the most important DM techniques, specific ANN architectures for dimensionality reduction, such as the principal components analysis ANN (PCA-ANN) and the linear auto-associative ANN (LA-ANN), are used on far fewer occasions. In this study, categorical principal component analysis (CATPCA) and the two ANN procedures are studied and compared searching for uniqueness in an applied context relative to personality variables and drug consumption. A sample of 7,030 adolescents completed a personality test made up of 20 dichotomous items with a hypothesized four-factor latent model. Results point out that both ANN factor solutions converge to those obtained using CATPCA. Nevertheless, possible drawbacks of the ANN techniques lie in their relatively complex application, as well as in the need to use visual graphic analysis as a support for interpreting the factorized solutions.