Hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory
DOI:
https://doi.org/10.35335/emod.v16i1.53Keywords:
Adaptive fuzzy grid partitioning, Customer segmentation, Hybrid approach, Rough set theory, Rule generationAbstract
This research proposes a hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory to address the problem of customer segmentation based on purchasing behavior. The objective is to minimize the fuzziness of the partitioning while maximizing the accuracy and interpretability of the generated rules. The research utilizes a dataset consisting of customer transactions, including demographics, purchase details, and satisfaction ratings. The fuzzy grid partitioning process divides the customer space into grid cells, representing different segments. Fuzzy membership values are assigned to data points based on their association with each grid cell. Rough set theory is employed for attribute reduction, identifying the most relevant attributes for customer segmentation. Rule induction algorithms generate rules that capture the patterns and dependencies among customer attributes and their association with specific grid cells. The hybrid approach combines the advantages of adaptive fuzzy grid partitioning and rough set-based rule generation. The optimization process adjusts fuzzy membership values and refines the generated rules to improve accuracy and interpretability. A numerical example and a case study in the retail industry are presented to demonstrate the effectiveness of the proposed approach. Results show successful customer segmentation and generation of actionable rules for marketing strategies. The research contributes to the field of customer segmentation by providing a comprehensive methodology that integrates adaptive fuzzy grid partitioning and rule generation using rough set theory. The hybrid approach offers valuable insights into customer behavior, enabling targeted marketing campaigns, personalized recommendations, and enhanced customer satisfaction.
References
Ahmed, M. M., & Isa, N. A. M. (2017). Knowledge base to fuzzy information granule: A review from the interpretability-accuracy perspective. Applied Soft Computing, 54, 121–140.
Azam, M. H., Hasan, M. H., Hassan, S., & Abdulkadir, S. J. (2021). A novel approach to generate type-1 fuzzy triangular and trapezoidal membership functions to improve the classification accuracy. Symmetry, 13(10), 1932.
Bechini, A., Marcelloni, F., & Renda, A. (2020). TSF-DBSCAN: a novel fuzzy density-based approach for clustering unbounded data streams. IEEE Transactions on Fuzzy Systems, 30(3), 623–637.
Cao, B., Zhao, J., Lv, Z., Gu, Y., Yang, P., & Halgamuge, S. K. (2020). Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction. IEEE Transactions on Fuzzy Systems, 28(5), 939–952.
Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9, 1–13.
Cheruku, R., Edla, D. R., Kuppili, V., & Dharavath, R. (2018). Rst-batminer: A fuzzy rule miner integrating rough set feature selection and bat optimization for detection of diabetes disease. Applied Soft Computing, 67, 764–780.
Daniel, P. A., & Daniel, C. (2018). Complexity, uncertainty and mental models: From a paradigm of regulation to a paradigm of emergence in project management. International Journal of Project Management, 36(1), 184–197.
Del Élisabethville, P. B., & Del Norte, M. (2019). Hybridizing Grid Partitioning and Rough Set Method for Fuzzy Rule Generation: A Robust Framework for Dataset Classification with Enhanced Interpretability and Scalability. International Journal of Enterprise Modelling, 13(3), 130–145.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71.
Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing, 93, 106282.
Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108–141.
Hossain, T. M., Watada, J., Aziz, I. A., & Hermana, M. (2020). Machine learning in electrofacies classification and subsurface lithology interpretation: A rough set theory approach. Applied Sciences, 10(17), 5940.
Kang, X. (2021). Combining rough set theory and support vector regression to the sustainable form design of hybrid electric vehicle. Journal of Cleaner Production, 304, 127137.
Kornelisius, C., Caeyso, E., & Feh, C.-G. (2019). Grid Partitioning And Rough Set Method Approach For Fuzzy Rule Generation. International Journal of Enterprise Modelling, 13(1), 30–39.
Li, Y., Liu, H., Liu, G., Li, L., Moore, P., & Hu, B. (2017). A grouping method based on grid density and relationship for crowd evacuation simulation. Physica A: Statistical Mechanics and Its Applications, 473, 319–336.
Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2016). Visualizing high-dimensional data: Advances in the past decade. IEEE Transactions on Visualization and Computer Graphics, 23(3), 1249–1268.
Luperto, M., Antonazzi, M., Amigoni, F., & Borghese, N. A. (2020). Robot exploration of indoor environments using incomplete and inaccurate prior knowledge. Robotics and Autonomous Systems, 133, 103622.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: architecture, opportunities, and open research challenges. Ieee Access, 5, 5247–5261.
Nanda, N. B., & Parikh, A. (2019). Hybrid approach for network intrusion detection system using random forest classifier and rough set theory for rules generation. Advanced Informatics for Computing Research: Third International Conference, ICAICR 2019, Shimla, India, June 15–16, 2019, Revised Selected Papers, Part II 3, 274–287.
Nasiakou, A., Alamaniotis, M., Tsoukalas, L. H., & Vavalis, M. (2018). Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods. Handbook of Dynamic Data Driven Applications Systems, 505–526.
Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks–A review. Journal of King Saud University-Computer and Information Sciences, 31(4), 415–425.
Sadiq, M., & Susheela Devi, V. (2021). Prioritization and selection of the software requirements using rough-set theory. IETE Journal of Research, 1–18.
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data, 7, 1–29.
Sikder, I. U. (2016). A variable precision rough set approach to knowledge discovery in land cover classification. International Journal of Digital Earth, 9(12), 1206–1223.
Slim, H., & Nadeau, S. (2020a). A mixed rough sets/fuzzy logic approach for modelling systemic performance variability with FRAM. Sustainability, 12(5), 1918.
Slim, H., & Nadeau, S. (2020b). A proposition for combining rough sets, fuzzy logic and FRAM to address methodological challenges in safety management: A discussion paper. Safety, 6(4), 50.
Surový, P., & Kuželka, K. (2019). Acquisition of forest attributes for decision support at the forest enterprise level using remote-sensing techniques—A review. Forests, 10(3), 273.
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020). A comprehensive survey of anomaly detection techniques for high dimensional big data. Journal of Big Data, 7, 1–30.
Wang, L., Ding, S., Wang, Y., & Ding, L. (2021). A robust spectral clustering algorithm based on grid-partition and decision-graph. International Journal of Machine Learning and Cybernetics, 12, 1243–1254.
Zhang, L., Lu, W., Liu, X., Pedrycz, W., & Zhong, C. (2016). Fuzzy c-means clustering of incomplete data based on probabilistic information granules of missing values. Knowledge-Based Systems, 99, 51–70.
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Copyright (c) 2022 Pa Liu Zheng, Liu Wang Zhang, Li wang cheng, Koscik Xue Huang
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