Nowadays, extracting fuzzy concepts using fuzzy formal concept analysis (FFCA) is an increasingly important process in many fields including data-mining, information retrieval, and Ontology construction. However, recent studies have heightened the need for more efficient approaches for extracting a reduced count of distinct fuzzy concepts in a reasonable time. This paper aims mainly to address such challenge. Generally, the proposed approach is composed of two main stages to handle quantitative data effectively. Firstly, a data-sensitive fuzzification stage maps the many-valued context to a more consistent fuzzy one. Secondly, an enhanced algorithm generates a reduced count of valuable fuzzy concepts and merges the more similar ones. Accordingly, the proposed approach can efficiently handle very large datasets where the process of extracting all fuzzy concepts is an intractable task. Surprisingly, the proposed approach reduces the overall processing time and complexity when compared with some other previous approaches.