Summary: With the advancement of data technology and storage services, the scale and complexity of data are rapidly growing. Consequently, promptly analyzing data and deriving precise insights have become urgent. Nevertheless, traditional methods struggle to balance the speed and accuracy of data mining. This paper proposes a data analysis technique called the Division-Mining-Fusion (DMF) strategy to tackle this challenge. Specifically, we divide a large-scale and complex dataset into multiple small-scale and simple sub-datasets. Then, we extract the knowledge embedded within each sub-dataset. Finally, we combine the extracted knowledge from each sub-dataset to accomplish learning tasks. To demonstrate the superior performance of the DMF strategy, we apply it to two fields: rough set theory and feature selection. The DMF strategy can accelerate the speed of data mining, enhance the accuracy of data analysis, and reduce the dimensionality of data. These advantages suggest that the DMF strategy outperforms traditional methods in processing data more efficiently. In addition, the number of sub-datasets is a crucial parameter of the DMF strategy. As the number of sub-datasets increases, the ability of the DMF strategy to analyze data continuously improves.