摘要 Abstract 

Data mining involves the discovery of meaningful rules and patterns from large datasets. Its importance has grown substantially in the era of big data, as it provides users with concise and relevant insights rather than overwhelming amounts of raw information. Meanwhile, other research areas—such as fuzzy sets, metaheuristics, and federated learning—have attracted significant attention in recent years for their ability to enhance the effectiveness and efficiency of computational systems, even when the resulting solutions are not strictly optimal. In this talk, I aim to integrate these techniques with data mining to address various types of mining problems. Specifically, I will introduce the concepts of fuzzy data mining, genetic fuzzy data mining, federated data mining, the duality of data mining, and several other intriguing topics that demonstrate how different methods can be combined to create more flexible and powerful mining frameworks.