Abstract

Fuzzy data mining techniques provide robust solutions for managing uncertainty, imprecision and interpretability challenges in complex datasets. This review analyses four prominent methodologies: Fuzzy Neural Networks (FNN), Fuzzy Decision Trees (FDT), Fuzzy Genetic Algorithms (FGA) and Fuzzy Rule Extraction (FRE). FNNs integrate neural learning with fuzzy reasoning to improve pattern recognition and decision-making; FDTs enhance classification under ambiguous conditions; FGA optimizes solutions in uncertain environments; and FRE generates interpretable rules from complex models. Despite their effectiveness, these methods face limitations in scalability, rule explosion and the interpretability–accuracy trade-off. To overcome these challenges, the paper introduces Hybrid Deep Fuzzy Intelligence (HDFI), a next-generation approach that integrates deep learning architectures with fuzzy reasoning. HDFI enables scalable, high-dimensional feature extraction while maintaining human-understandable fuzzy rules, thereby bridging the gap between predictive accuracy and interpretability. Applications span healthcare, finance, industrial IoT, manufacturing and cybersecurity, where real-time and explainable decision-making are crucial. The paper highlights the advantages of these fuzzy approaches, recent advancements, research gaps and emerging trends, emphasizing HDFI as a promising direction for future intelligent data mining systems.

References

• Ł. Apiecionek, "Fuzzy Neural Networks—A Review with Case Study," Applied Sciences, vol. 15, no. 13, p. 6980, Jun. 2025 .
• O. Syzonov, S. Tomasiello and N. Capuano, "New Insights into Fuzzy Genetic Algorithms for Optimization Problems," Algorithms, vol. 17, no. 12, p. 549, Dec. 2024.
• M. Al-Shammari and A. Al-Abadi, "Combining Fuzzy Logic and Genetic Algorithms to Optimize Cost, Time and Quality in Modern Agriculture," Sustainability, vol. 17, no. 7, p. 2829, Mar. 2025.
• J. Zhan and S. Alcantud, "Fuzzy Decision Tree Based on Fuzzy Rough Sets and Z-Number Rules," Entropy, vol. 13, no. 12, p. 836, Nov. 2024.
• Y. Ren, X. Zhu, K. Bai and R. Zhang, "A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees," arXiv preprint, Mar. 2024.
• M. Samhouri and S. Z. Qareish, "Hybrid Fuzzy Genetic Algorithm for the Integration of Process Planning and Scheduling for Distributed Flexible Job Shop," Neural Computing and Applications, vol. 36, no. 4, pp. 102–115, 2024.
• P. V. de Campos Souza, "Fuzzy neural networks and neuro-fuzzy networks: A review," Neurocomputing, vol. 415, pp. 1–12, 2020.
• J. Tavoosi, "A review on type-2 fuzzy neural networks for system identification," Computers in Biology and Medicine, vol. 137, p. 104748, 2021.
• J. I. Obianyo, "Application of neural networks and neuro-fuzzy models in construction scheduling," Scientific Reports, vol. 13, p. 35445, 2023.
• B. Luo, "Application and empirical analysis of fuzzy neural networks in social media user behavior analysis," Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 1–10, 2024.
• A. Sharma and P. Jain, "Limitations of classical decision tree algorithms in uncertain data environments," Information Sciences, vol. 586, pp. 412–423, 2022.
• M. R. Akbari and S. A. Hosseini, "Overfitting challenges in traditional decision trees: A review," Applied Soft Computing, vol. 124, p. 109114, 2022.
• R. Das and M. Ahmed, "Fuzzy decision tree models for imprecise data classification," Applied Soft Computing, vol. 128, p. 109495, 2022.
• L. Wu, Q. Li and F. Xu, "Enhanced classification using fuzzy decision trees with smooth boundaries," Neurocomputing, vol. 560, pp. 112–124, 2025.
• H. Chen, T. Zhou and Y. Wang, "Uncertainty-aware fuzzy decision trees in healthcare and finance," IEEE Access, vol. 11, pp. 98562–98574, 2023.
• X. Ran, Y. Zhang and Z. Liu, "A novel fuzzy system-based genetic algorithm for trajectory planning," Computers, Materials & Continua, vol. 70, no. 1, pp. 1–15, 2025.
• Y. Zhang, "Financial data mining and information disclosure using fuzzy logic algorithms," Journal of Financial Data Science, vol. 1, no. 1, pp. 1–15, 2025.
• A. Sahu, S. S. S. R. Depuru and S. K. S. Gupta, "Fuzzy inference system using genetic algorithm and pattern search for roof fall prediction," Journal of Mining Science, vol. 60, no. 2, pp. 1–10, 2024.
• S. K. Patel and R. Sharma, "Comparative study of fuzzy genetic algorithm and genetic algorithm for data mining applications," IEEE Transactions on Computational Intelligence and AI in Games, vol. 13, no. 2, pp. 375–384, 2022.
• D. Dubois, "What are fuzzy rules and how to use them," Fuzzy Sets and Systems, vol. 84, no. 2, pp. 169–185, 1996.
• M. Holena, "Extraction of fuzzy logic rules from data by means of artificial neural networks," Kybernetika, vol. 41, no. 3, pp. 297–314, 2005.
• J. Hao, J. Wang and J. Liu, "Rule extraction from biased random forest and fuzzy support vector machine," Scientific Reports, vol. 12, no. 1, p. 10457, 2022.
• X. Tan, Q. Chang and N. R. Pal, "Selecting correct methods to extract fuzzy rules from artificial neural networks," Mathematics, vol. 9, no. 11, p. 1164, 2021.
• G. Xue, Q. Chang, J. Wang, K. Zhang and N. R. Pal, "An adaptive neuro-fuzzy system with integrated feature selection and rule extraction for high-dimensional classification problems," arXiv preprint arXiv:2201.03187, 2022.
• B. Wang, "A Hybrid Fuzzy Logic and Deep Learning Model for Corpus-Based German Learning," Informatica, vol. 49, no. 1, pp. 1–12, 2025.
• R. J. AL-Sukeinee and R. S. Khudayer, "Review: Deep Learning and Fuzzy Logic Applications," Advances in Intelligent Systems and Computing, vol. 4, no. 3, pp. 292–313, 2024.
• D. Shrivastava et al., "Internet of Things Driven Hybrid Neuro-Fuzzy Deep Learning for Energy Management," Frontiers in Artificial Intelligence, vol. 8, pp. 1–12, 2025.
• M. Yeganejou et al., "DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System," arXiv preprint arXiv:2308.06378, 2023.