Knowledge discovery in databases based on deep neural networks

Knowledge discovery in databases (KDD) has received great progress in recent years for the need of mining useful knowledge in the ever growing information. The advances in machine learning technologies effectively promote KDD in the procedures of feature extraction and data categorization. This paper introduces a framework that combines feature extraction and categorization of the collected data in order to recognize useful structured patterns that underlies the raw data. This frame work consists of three modules: data pre-processing module, feature extraction module, and feature classification module. We propose a four-layered deep neural network as the feature extraction architecture. Each layer is trained in an unsupervised way as one auto-encoder with sparsity constraint. We employ a softmax classifier to assign a label to the extracted feature. The supervised and unsupervised training strategies are discussed at the end of this paper to disambiguate the training procedure of the entire model.