Relevant Attributes in Formal Contexts
This work addresses a computational bottleneck in formal concept analysis for researchers and practitioners dealing with large-scale data, representing an incremental advancement by adapting machine learning techniques to this domain.
The paper tackles the computational challenge of constructing formal concept lattices from massive datasets by introducing a method for attribute selection, which reduces the number of attributes while preserving the lattice structure, though no concrete performance numbers are provided.
Computing conceptual structures, like formal concept lattices, is in the age of massive data sets a challenging task. There are various approaches to deal with this, e.g., random sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this end, we propose the notion of relevant attributes which enables us to define a relative relevance function, reflecting both the order structure of the concept lattice as well as distribution of objects on it. Finally, we overcome computational challenges for computing the relative relevance through an approximation approach based on information entropy.