The field of fuzzy logic and data mining is experiencing significant growth, with a focus on developing innovative methods for handling complex, high-dimensional data. Researchers are exploring the application of fuzzy logic to real-world problems, such as scheduling, biofilter performance prediction, and stock trend prediction. The integration of fuzzy logic with other techniques, like genetic algorithms and neural networks, is also being investigated. Additionally, there is a trend towards developing more efficient and scalable data mining algorithms, including those for clustering, rule mining, and statistical analysis. Notable papers in this area include: EnviroPiNet, which presents a physics-guided AI model for predicting biofilter performance, achieving an R^2 value of 0.9236 on the testing dataset. A Dynamic Fuzzy Rule and Attribute Management Framework, which introduces an Adaptive Dynamic Attribute and Rule framework for fuzzy inference systems in high-dimensional data, showing consistently lower Root Mean Square Error compared to state-of-the-art baselines. RuleKit 2, which significantly improves the computational performance of rule-based data analysis, reducing analysis time by two orders of magnitude. TNStream, which proposes a clustering algorithm based on the novel concept of Tightest Neighbors, adaptively determining the clustering radius based on local similarity and improving clustering quality for multi-density data.