Analysis of Social Media Networks based on Self-organizing Feature Maps Approach for Social E-commerce Networks | ||||
Journal of the ACS Advances in Computer Science | ||||
Article 1, Volume 12, Issue 1, 2021, Page 1-9 PDF (343.61 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/asc.2021.240047 | ||||
View on SCiNiTO | ||||
Authors | ||||
Mohamed E. A. Ebrahim1; Lamia Fattouh Ibrahim2; Hesham A. Salman3 | ||||
1Computer Science Department Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt | ||||
2Faculty of information systems and computer science of October 6 University, Giza, Egypt; Computer Science Department Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt | ||||
3Computer Science Department, Higher Institute of Computers and Information Technology, Alshrouk Academy, Cairo, Egypt, | ||||
Abstract | ||||
Kohonen’s algorithm is a computational data analysis method. Social E-commerce mining requires human data analysts and automated software programs to sift through massive amounts of raw social media data. Social commerce networks using user frequent pattern mining. This paper focused on discern patterns and trends relating to social media usage based on self-organizing map SOM algorithm for analysis of social media networks and websites to get user access patterns characteristics. | ||||
Keywords | ||||
Kohonen’s algorithm; Social E-commerce network; web mining; neural network | ||||
Statistics Article View: 170 PDF Download: 158 |
||||