Intelligent approach for large-scale data mining
Khaled M. Fouad, Doaa L. El Bably
Abstract
Large-scale data mining has become a very difficult issue using traditional methods because the data complexity is very high. In the proposed approach, an integration of three methods; Optimised Principal Component Analysis (OPCA), Optimised Enhanced Extreme Learning Machine (OEELM), and stratified sampling, called OPCA-EELM2SS, is presented to provide intelligent and enhanced large-scale data mining. OPCA provides a good representation of large-scale data sets by using the Stratified Sample (SS). By using OEELM, the optimal number of Hidden Nodes (HNs) in ELM is exploited to build a single hidden layer feedforward neural network (SLFN). The proposed approach is tested by using nineteen benchmark data sets. The experimental results demonstrate the effectiveness of the proposed approach by performing different experiments for classical PCA and Independent Component Analysis (ICA), which are integrated with the enhanced ELM using different evaluation criteria. For more reliability, the proposed approach is compared with many previous methods.