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.

中文翻译：

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一种基于划分-挖掘-融合策略的数据分析方法

随着数据技术和存储服务的进步，数据的规模和复杂性正在快速增长。因此，及时分析数据并得出精确的见解变得刻不容缓。然而，传统方法很难平衡数据挖掘的速度和准确性。本文提出了一种称为分割-挖掘-融合（DMF）策略的数据分析技术来应对这一挑战。具体来说，我们将一个大规模且复杂的数据集划分为多个小规模且简单的子数据集。然后，我们提取每个子数据集中嵌入的知识。最后，我们结合从每个子数据集中提取的知识来完成学习任务。为了证明DMF策略的优越性能，我们将其应用于两个领域：粗糙集理论和特征选择。 DMF策略可以加快数据挖掘的速度，增强数据分析的准确性，降低数据的维度。这些优点表明 DMF 策略在更有效地处理数据方面优于传统方法。此外，子数据集的数量是DMF策略的一个关键参数。随着子数据集数量的增加，DMF策略分析数据的能力不断提高。