DyadChurn-Reference-Cited by-同舟云学术

DyadChurn

Published:2017 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017
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Author:

Abd-Allah Marwa N.¹,El-Beltagy Samhaa R.²,Salah Akram¹

Affiliation:

1. Dept. of Computers and Information, Cairo University, Egypt

2. Dept. of Computers and Information, Nile University, Egypt

Publisher

ACM Press

Reference29 articles.

1. Abd-Allah, M.N., Salah, A. and El-Beltagy, S.R. 2014. Enhanced Customer Churn Prediction using Social Network Analysis. Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media (2014), 11--12.

2. Ahn, J.-H., Han, S.-P. and Lee, Y.-S. 2006. Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications Policy. 30, 10 (Nov. 2006), 552--568.

3. Berger, E. 2001. Dynamic monopolies of constant size. Journal of Combinatorial Theory, Series B. 83, 2 (2001), 10.

4. Clemente, M., Giner-Bosch, V. and San Matías, S. 2010. Assessing classification methods for churn prediction by composite indicators. Manuscript, Dept. of Applied Statistics, OR & Quality, Universitat Politècnica de València, Camino de Vera s/n. 46022, (2010).

5. Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A. and Joshi, A. 2008. Social ties and their relevance to churn in mobile telecom networks. Proceedings of the 11th international conference on Extending database technology: Advances in database technology (New York, New York, USA, 2008), 668--677.

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1. Unveiling the Power of Social Influence: A Machine Learning Framework for Churn Prediction With Network Analysis;IEEE Access;2024

2. Feature Selection for Customer Churn Prediction: A Review on the Methods & Techniques applied in the Telecom Industry;2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2022-04-23

3. Comparison of Main Algorithms in Big Data Analysis of Telecom Customer Retention;IOP Conference Series: Materials Science and Engineering;2021-02-01

4. Churners Prediction Using Relational Classifier Based on Mining the Social Network Connection Structure;Communications in Computer and Information Science;2020

5. Using Big Data Analysis to Retain Customers for Telecom Industry;Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence - ICCAI '19;2019