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.
Formal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice. Usually, it is an NP‐problem and consumes plenty of time and storage space to update the changes of the lattice. Thus, introducing an efficient way to update and maintain such lattices is a significant area of interest within the field of FCA and its applications. One of those vital FCA applications is the association rule mining (ARM), which aims at generating a loss‐less nonredundant compact Association Rule‐basis (AR‐basis). Currently, the real‐world data rapidly overgrow that asks the need for updating the existing concept lattice and AR‐basis upon data change continually. Intuitively, updating and maintaining an existing concept‐lattice or AR‐basis is much more efficient and consistent than reconstructing them from scratch, particularly in the case of massive data. So far, the area of updating both concept lattice and AR‐basis has not received much attention. Besides, few noncomprehensive studies have focused only on updating the concept lattice. From this point, this article comprehensively introduces basic knowledge regarding updating both concept lattices and AR‐basis with new illustrations, formalization, and examples. Also, the article reviews and compares recent remarkable works and explores the emerging future research trends. This article is categorized under: Algorithmic Development > Association Rules Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Association Rules
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