FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables representing a fuzzy context as a pattern structure. Studying the relation between different extensions of FCA is of high importance, since it allows migrating methods from one extension to another. Moreover, it allows for more simple implementation of different extensions within a software.