Title: Optimising the mining strategy of web page based on ant colony algorithm of information entropy

Authors: Meiwen Guo; Jianping Peng; Yuanping Zhang; C.H. Chiu; Liang Wu

Addresses: School of Management, Xinhua College of Sun Yet-sen University, Guangzhou 510520, China ' Business School, Sun Yet-sen University, Guangzhou 510275, China ' School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China ' Business School, Sun Yet-sen University, Guangzhou 510275, China ' School of Management, Xinhua College of Sun Yet-sen University, Guangzhou 510520, China

Abstract: The speed and quality for browsers to obtain page information are determined by the accuracy degree of web page information filtering. This research improved ant colony algorithm, introducing the information entropy with the ability to judge the probability of occurrence of information and adjusting its operation order. The study uses Sina homepage information from January 2017 to August as a sample, four indexes are used to evaluate the improved algorithm, which are maximum iterations, average execution time, average error rate and error percentage. It is found that the four indexes of improved algorithm have better effect on the precision of information mining than before, and the cost of this method has not increased significantly. This algorithm is used to provide web page information layout as well as information placement strategies, so as to help website operators and web page designers to further enhance the design and operation efficiency.

Keywords: data mining; ant colony algorithm; ACS; information entropy.

DOI: 10.1504/IJRIS.2019.103524

International Journal of Reasoning-based Intelligent Systems, 2019 Vol.11 No.4, pp.308 - 318

Received: 10 Feb 2018
Accepted: 03 Nov 2018

Published online: 05 Nov 2019 *

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