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Logic mining for Telecommunication churn classification: Permutation Weighted Random 2 Satisfiability Reverse Analysis approach


Citation

Zamri, Nur Ezlin and Jamil, Nurul Ain Najwa Mohamad and Romli, Nurul Atiqah and Kasihmuddin, Mohd Shareduwan Mohd (2026) Logic mining for Telecommunication churn classification: Permutation Weighted Random 2 Satisfiability Reverse Analysis approach. Barekeng, 20 (3). pp. 2375-2388. ISSN 1978-7227; eISSN: 2615-3017

Abstract

The telecommunications industry is experiencing rapid transformation, resulting in tense competition and increased customer volatility. Telecom churn, which refers to the discontinuation of services by customers, poses a serious challenge due to its direct impact on revenue and long-term profitability. Addressing this issue requires effective methods for understanding and predicting customer behavior. Hence, a logic mining approach is introduced in this study, namely the Permutation Weighted Random 2 Satisfiability Reverse Analysis Method, to classify customer churn in the telecommunications sector. The proposed method is based on a logical rule known as Weighted Random 2 Satisfiability, which is implemented in the Discrete Hopfield Neural Network. The logical rule facilitates the dynamic allocation of negative literals, contributing to improved logical representation. Furthermore, the Election algorithm is incorporated during the training phase to enhance the accuracy of logical structure interpretation. The proposed method is capable of extracting optimal data patterns and generating induced logic that accurately describes customer churn behaviour. This induced logic not only predicts whether a customer will churn but also provides interpretable insights into the underlying causes. Experimental results demonstrate a strong average accuracy of 85.6%, indicating the effectiveness and scalability of the proposed approach for knowledge discovery. Although the proposed approach achieves strong accuracy, the lower F1-Score and Matthews Correlation Coefficient reveal limitations in churn customer classification, highlighting the need for further improvement in handling class imbalance. This study contributes to the field of data mining by offering a logic-based framework for churn classification and emphasizing its practical relevance in supporting strategic customer retention efforts in a competitive telecommunications sector.


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Additional Metadata

Item Type: Article
Subject: Mathematics (miscellaneous)
Subject: Numerical Analysis
Subject: Statistics, Probability and Uncertainty
Divisions: Faculty of Science
DOI Number: https://doi.org/10.30598/barekengvol20iss3pp2375-2388
Publisher: Universitas Pattimura
Keywords: Data mining; Discrete hopfield neural network knowledge classification; Logic mining; Telecommunication churn; Telecommunications sector
Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure, SDG 12: Responsible Consumption and Production, SDG 17: Partnerships for the Goals
Depositing User: Ms. Siti Radziah Mohamed@mahmod
Date Deposited: 13 May 2026 00:29
Last Modified: 13 May 2026 00:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.30598/barekengvol20iss3pp2375-2388
URI: http://psasir.upm.edu.my/id/eprint/125484
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