Title: Intelligent approach for large-scale data mining

Authors: Khaled M. Fouad; Doaa L. El-Bably

Addresses: Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt ' Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

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.

Keywords: principal component analysis; extreme learning machine; particle swarm optimisation; large-scale data mining.

DOI: 10.1504/IJCAT.2020.107906

International Journal of Computer Applications in Technology, 2020 Vol.63 No.1/2, pp.93 - 113

Received: 22 Jul 2019
Accepted: 24 Oct 2019
Published online: 30 Jun 2020 *

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