Efficient neighborhood function and learning rate of self-organizing map (SOM) for cell towers traffic clustering
Other Title(s)
وظيفة الجوار و معدل التعلم الفعال الخاص بخريطة التنظيم الذاتي (SOM) لتجميع حركة المرور في الأبراج الخلوية
Joint Authors
al-Mijibli, Intisar Shadid
al-Jabburi, Abbas Isa
Hammud, Haydar Kazim
Source
al-Qadisiyah Journal for Computer Science and Mathematics
Issue
Vol. 9, Issue 2 (31 Dec. 2017), pp.122-130, 9 p.
Publisher
University of al-Qadisiyah College of computer Science and Information Technology
Publication Date
2017-12-31
Country of Publication
Iraq
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
The self-organizing map (SOM) neural network is based on unsupervised learning, and has found variety of applications.
It is necessary to adjust the SOM parameters before starting learning process to ensure the best results.
In this research, three types of data represent high and low traffic of specific cell tower with subscriber positions distribution in central of Iraq are investigated by self-organizing map (SOM).
SOM functions and parameters influence its final results.
Hence, several iteration of experiments are performed to test and analyze Bubble, Gaussian and Catgass neighborhood functions with three learning rates (linear, inverse of time and power series) and they were evaluated based on the quantization error.
The experiments results show that Bubble function with linear learning rate gives the best result for clustering cell tower traffic.
American Psychological Association (APA)
Hammud, Haydar Kazim& al-Mijibli, Intisar Shadid& al-Jabburi, Abbas Isa. 2017. Efficient neighborhood function and learning rate of self-organizing map (SOM) for cell towers traffic clustering. al-Qadisiyah Journal for Computer Science and Mathematics،Vol. 9, no. 2, pp.122-130.
https://search.emarefa.net/detail/BIM-795373
Modern Language Association (MLA)
Hammud, Haydar Kazim…[et al.]. Efficient neighborhood function and learning rate of self-organizing map (SOM) for cell towers traffic clustering. al-Qadisiyah Journal for Computer Science and Mathematics Vol. 9, no. 2 (2017), pp.122-130.
https://search.emarefa.net/detail/BIM-795373
American Medical Association (AMA)
Hammud, Haydar Kazim& al-Mijibli, Intisar Shadid& al-Jabburi, Abbas Isa. Efficient neighborhood function and learning rate of self-organizing map (SOM) for cell towers traffic clustering. al-Qadisiyah Journal for Computer Science and Mathematics. 2017. Vol. 9, no. 2, pp.122-130.
https://search.emarefa.net/detail/BIM-795373
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 129
Record ID
BIM-795373