Abstract
Formal Concept Analysis (FCA) is a popular method for knowledge discovery and data mining in binary data. A shortcoming of FCA is the huge number of formal concepts that may be drawn from formal contexts (binary data tables) of moderate size or larger. A strategy to deal with this shortcoming is to extract a subset of formal concepts that cover the context either fully or partially. We compare different methods for generating full and partial concept cover, present a simple Greedy Coverage (GC) method, and show that it is an efficient option, especially for generating partial concept cover.
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