Data Mining Techniques for Web Mining: A Survey

Authors

  • Mehdi Gheisari Department of computer science and Technology, Islamic Azad University, Iran https://orcid.org/0000-0002-5643-0021
  • Hooman Hamidpour Department of Computer Engineering and Information Technology, Shiraz University of Technology, Iran https://orcid.org/0000-0002-2658-2075
  • Yang Liu Department of Computer science and Technology, Harbin Institute of Technology, China https://orcid.org/0000-0003-2486-5765
  • Peyman Saedi Oracle DBA at Behestan Rayan hamrah, Iran
  • Arif Raza School of Computer Science and Software Engineering, Shenzhen University, China
  • Ahmad Jalili Department of Computer Engineering, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Iran
  • Hamidreza Rokhsati Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
  • Rashid Amin Department of Computer Science, University of Chakwal, Pakistan https://orcid.org/0000-0002-3143-689X

DOI:

https://doi.org/10.47852/bonviewAIA2202290

Keywords:

social networks, web design, data mining, web mining, World Wide Web

Abstract

The data mining (DM) is the computational process that consists of searching, extracting, and analyzing patterns in large data sets, including methods at the intersection of artificial intelligence, machine learning, statistics, and database schemes. Specifically, its primary goal is to extract information from a raw data set and transform it into an expected structure for further use. Moreover, an evolving perspective of DM is web mining (WM), which refers to the whole of DM and related routines. It is used to discover and extract information from web records and services automatically, that is, WM’s purpose is to obtain valuable data from the World Wide Web. Due to its importance, a survey about DM techniques in WM is necessary, as performed in this paper.

 

Received: 30 June 2022 | Revised: 30 September 2022 | Accepted: 25 October 2022

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


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Published

2022-10-25

Issue

Vol. 1 No. 1 (2023)

Section

Review

License

Copyright (c) 2022 Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Gheisari, M., Hamidpour, H. ., Liu, Y. ., Saedi, P., Raza, A., Jalili, A., & Rokhsati, H. (2022). Data Mining Techniques for Web Mining: A Survey (R. Amin , Trans.). Artificial Intelligence and Applications, 1(1), 3-10. https://doi.org/10.47852/bonviewAIA2202290