粗糙集三支决策理论及其应用

利用粗糙集中的三支决策思想,将类用正域、负域和边界域刻画,得到初始聚类结果。然后通过定义重叠度和类与类的合并策略,将初始聚类结果进行合并,得到最终聚类结果。之后应用2个关系网络数据展示了具体的聚类步骤,并通过比较2个例子的聚类结果,分析了影响聚类结果的一个主要因素:阈值的选取。实验表明:阈值的选取对简单的网络结构数据集的聚类结果的影响并不明显,然而对复杂的网络结构数据集的聚类结果的影响则较为显著。

Three-way Decision Theory in Rough Sets and Its Application

By using the three-way decision ideas in rough sets, the class is characterized by positive domain, negative domain and boundary domain to get the initial clustering results. The final clustering result was merged by defining the degree of overlap and the merging strategy of class. The two given relational network data have shown the specific clustering steps, and by comparing the clustering results of two examples, a major factor influencing the clustering results is arisen: the selection of threshold value. Experiments show that: the effect of threshold value selection on clustering results of simple network structure datasets is not obvious, but the impact of clustering results on complex network structure datasets is more significant.