Title: Web mining based on word-centric search with clustering approach using MLP-PSO hybrid

Authors: Reza Samizadeh; Samaneh Tafahomi

Addresses: Department of Industrial Engineering, Alzahra University, Tehran, Iran ' Department of Industrial Engineering, Alzahra University, Tehran, Iran

Abstract: With web development, sometimes in keeping track of information on the web, the semantic meaning of words is not important, and the mere presence of words in the text is enough to extract information. In this research, the word-centric search method is presented to prepare web data for clustering. Multi-layer perceptron networks are one of the most successful neural networks for learning, clustering and prediction. The researcher clusters the web data from the word-centric search method by using the K-means method and considers the results of clustering as the expected output of the MLP neural network. Considering that the weights of the neural network are selected randomly, it may not be in the best amount after the network training. Therefore, by using an optimisation algorithm for particle swarm, its effect on performance of the final neural network has been investigated in the training and initial weighing step.

Keywords: web mining; clustering; multi-layer perceptron neural networks; particle swarm optimisation algorithm.

DOI: 10.1504/IJBIDM.2022.119980

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.1, pp.35 - 55

Received: 16 Feb 2019
Accepted: 30 Nov 2019

Published online: 17 Dec 2021 *

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