Search

Formal Concept Analysis (FCA) is a key tool in data analysis and knowledge discovery, yet its application is challenged by the complexity of concept lattices in large datasets. This paper presents the Kernel Concept Set Approach (KCS), a novel methodology that overcomes the limitations of traditional lattice reduction techniques by integrating a flexible derivation cost function and focusing on the frequency and structural importance of concepts. Unlike conventional methods, KCS efficiently operates in a general metric space, reducing computational costs and providing a dynamic approach to conceptual clustering. A comparative study with the K-means Dijkstra on Lattice (KDL) method highlights KCS's superiority in simplifying lattice complexity and enhancing clustering quality. KCS not only maintains crucial data structures but also facilitates the approximation of formal concept lattices, establishing it as an efficient alternative for structured data analysis. [ABSTRACT FROM AUTHOR]