Knowledge Cores in Large Formal Contexts
This work addresses the challenge of interpretable knowledge extraction in large formal contexts for researchers in data mining and FCA, offering a structurally motivated alternative to existing methods.
The paper tackles the problem of infeasible knowledge computation in large formal concept analysis (FCA) data sets by introducing knowledge cores based on $k$-cores from network science, which allows for comprehensible extraction of rare patterns without relying on random processes or high support thresholds.
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.