Knowledge Extraction by Fuzzy Association Rules: An Extension Approach to the Fuzzy Case

Abstract

Numerous studies have explored association rule extraction within the research domain. Yet, prevalent algorithms are typically constrained to processing binary data, indicating merely the presence or absence of elements. It is crucial to acknowledge that a significant portion of real-world data is quantitative and numeric.

In this article, we introduce an innovative approach aimed at accommodating such data types through the incorporation of fuzzy logic. Our proposed algorithm is tailored to extract fuzzy association rules, offering a more nuanced and flexible perspective on the relationships within quantitative and numeric datasets.