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Vijayanand, N.
- Rough Sets-based Decision Support Systems for Customer Relationship Management
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Authors
Affiliations
1 Department of Management Studies, Pondicherry University, IN
1 Department of Management Studies, Pondicherry University, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3055-3060Abstract
Customer Relationship Management (CRM) plays a crucial role in maintaining and enhancing customer satisfaction and loyalty. To effectively manage customer relationships, organizations require decision support systems that can provide valuable insights and assist in making informed decisions. In this context, Rough Sets-Based Decision Support Systems (RS-DSS) have emerged as a promising approach. RS-DSS utilizes the principles of rough set theory to handle uncertainty and vagueness in customer data, enabling the discovery of hidden patterns and knowledge for effective CRM. This paper provides an overview of the application of RS-DSS in CRM, highlighting its benefits and challenges. The study also discusses various components of RS-DSS, including attribute reduction, rule extraction, and decision-making. Furthermore, the paper presents a case study to illustrate the practical implementation and potential outcomes of RS-DSS in CRM. Overall, RS-DSS holds significant potential in enhancing customer relationship management by leveraging rough set theory for decision support and improving organizational performance.Keywords
Customer Relationship Management (CRM), Decision Support Systems, Rough Set Theory, Rough Sets-Based Decision Support Systems (RS-DSS), Uncertainty, Vagueness, Attribute Reduction, Rule Extraction, Decision-Making, Organizational Performance.
References
- M.W. Moreira and N. Kumar, “A Comprehensive Review on Smart Decision Support Systems for Health Care”, IEEE Systems Journal, Vol. 13, No. 3, pp. 3536-3545, 2019.
- X. Zhou and W. Liang, “CNN-RNN based Intelligent Recommendation for Online Medical Pre-Diagnosis Support”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 18, No. 3, pp. 912-921, 2020.
- Y. Yao, “Three-Way Decision and Granular Computing”, International Journal of Approximate Reasoning, Vol. 103, pp. 107-123, 2018.
- Y. Yao, “Three-Way Granular Computing, Rough Sets, and Formal Concept Analysis”, International Journal of Approximate Reasoning, Vol. 116, pp. 106-125, 2020.
- Y. Yao, “The Geometry of Three-Way Decision”, Applied Intelligence, Vol. 51, No. 9, pp. 6298-6325, 2021.
- J. Zhan, W. Ding and P. Liu, “A Novel Three-Way Decision Model based on Utility Theory in Incomplete Fuzzy Decision Systems”, IEEE Transactions on Fuzzy Systems, Vol. 30, No. 7, pp. 2210-2226, 2021.
- M. Bhende and V. Saravanan, “Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection”, BioMed Research International, Vol. 2022, pp. 1-13, 2022.
- A. Campagner and D. Ciucci, “Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review”, Proceedings of International Joint Conference on Rough Sets, pp. 137-152, 2020.
- Y. Yao, Yao, “Three-Way Decisions and Cognitive Computing”, Cognitive Computation, Vol. 8, No. 4, pp. 543-554, 2016.
- Deveci, Muhammet, Pablo R. Brito-Parada, Dragan Pamucar and Emmanouil A. Varouchakis. “Rough Sets based Ordinal Priority Approach to Evaluate Sustainable Development Goals (SDGs) for Sustainable Mining”, Resources Policy, Vol. 79, pp. 1-14, 2022.
- S.H. Liao and Y.J. Chen, “Rough Sets based Association Rules Application for Knowledge-Based System Design”, Proceedings of International Joint Conference on Computational Collective Intelligence. Technologies and Applications, pp. 501-510, 2010.
- S. Maldonado and R. Weber, “Credit Scoring using Three-Way Decisions with Probabilistic Rough Sets”, Information Sciences, Vol. 507, pp. 700-714, 2020.
- F. Zhang and W. Ma, “Study on Chaotic Multi-Attribute Group Decision Making Based on Weighted Neutrosophic Fuzzy Soft Rough Sets”, Mathematics, Vol. 11, No. 4, pp. 1034-1044, 2023.