论文标题
在大型正式背景下的知识核心
Knowledge Cores in Large Formal Contexts
论文作者
论文摘要
对于大型数据集，知识计算任务通常是不可行的。当在形式概念分析（FCA）中得出知识基础时，这尤其是正确的。因此，必须提出解决这个问题的技术。许多成功的方法基于随机过程，以减少研究的数据集的大小。但是，这使得它们在发现的知识方面几乎无法解释。其他方法将自己限制在高度支持的子集中，并忽略了稀有而有趣的模式。网络科学中使用了一种基本不同的方法，称为$ k $ cors。如果数据集在数据集中良好连接，则可以反映稀有模式。在这项工作中，我们通过利用与双方图的自然对应关系来研究FCA领域的$ k $ cores。这种以结构上动机的方法导致从大型正式环境数据集中可以理解知识核心的可理解。
Knowledge computation tasks are often infeasible for large data sets. This is in particular true when deriving knowledge bases in formal concept analysis (FCA). Hence, it is essential to come up with techniques to cope with this problem. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and interesting patterns. An essentially different approach is used in network science, called $k$-cores. These are able to reflect rare patterns if they are well connected in the data set. In this work, we study $k$-cores in the realm of FCA by exploiting the natural correspondence to bi-partite graphs. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts data sets.
