Abstract
Formal concept analysis is an effective tool for granular computing. In applications, the selection of attributes greatly determines the structure and size of the obtained concept lattice. The existing studies have dealt with this problem on the basis of ordinary formal concepts. And thus, these studies are not capable of dealing with uncertainties caused by fuzziness. As a solution, we investigate attribute granulation in the settings of fuzzy contexts based on L-fuzzy concepts. Firstly, we present the mathematical descriptions of this open problem as well as the detailed meanings of this study with an illustrative example. And then, we present zoom-in algorithm to get more specific fuzzy concepts, and zoom-out algorithm to derive more abstract fuzzy concepts. Finally, systematic experiments are performed to show the influence of granulating of attributes on the sizes of fuzzy concept lattices.
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