R Mansouri
Application of data mining in predicting cell phones subscribers behavior employing the contact pattern
Mansouri, R; Saraee, MH; Amirfattahi, R
Abstract
As telecommunication services becoming competitive, client contract management in this sector has become importance as well. In regards to the fact that a huge volume of telecommunication data especially details of the cell phone conversations exist, and they are practically not used, employment of data mining techniques on such data lead to exploring the hidden knowledge in them on the subscriber's behavior and lead s to predicting their behavior. Therefore, data mining is one of the most crucial methods of scientific management effective on contact with client increased profitability and client satisfaction. In this paper, using details of the phone cell conversations during two periods (one with no rival and the second one with rival) and the employing details of conversations of the third period for identifying subscribes suffering churn, it has attempted, regarding the pattern of client to predict their churn.
Citation
Mansouri, R., Saraee, M., & Amirfattahi, R. (2010, February). Application of data mining in predicting cell phones subscribers behavior employing the contact pattern. Presented at DSDE, Bangalore, India
Presentation Conference Type | Other |
---|---|
Conference Name | DSDE |
Conference Location | Bangalore, India |
Start Date | Feb 9, 2010 |
End Date | Feb 10, 2010 |
Publication Date | Jan 1, 2010 |
Deposit Date | Oct 26, 2011 |
Publicly Available Date | Apr 5, 2016 |
Publisher URL | http://dx.doi.org/10.1109/DSDE.2010.57 |
Additional Information | Event Type : Conference |
Files
Accepted Version
(368 Kb)
PDF
You might also like
Features in extractive supervised single-document summarization: case of Persian news
(2024)
Journal Article
Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips
(2023)
Journal Article
DeepClean : a robust deep learning technique for autonomous vehicle camera data privacy
(2022)
Journal Article
Machine learning-based optimized link state routing protocol for D2D communication in 5G/B5G
(2022)
Presentation / Conference
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search