AGENTIC-SYSTEMSApril 14, 20261 min read15 views

Factorizing formal contexts from closures of necessity operators

This research explores factorizing complex datasets into independent subcontexts using necessity operators. It extends traditional Boolean data methods to fuzzy frameworks, enhancing computational efficiency in AI.

Computer Science > Artificial Intelligence

Title:Factorizing formal contexts from closures of necessity operators

View PDF HTML (experimental)Abstract:Factorizing datasets is an interesting process in a multitude of approaches, but many times it is not possible or efficient the computation of a factorization of the dataset. A method to obtain independent subcontexts of a formal context with Boolean data was proposed in~\cite{dubois:2012}, based on the operators used in possibility theory. In this paper, we will analyze this method and study different properties related to the pairs of sets from which a factorization of a formal context arises. We also inspect how the properties given in the classical case can be extended to the fuzzy framework, which is essential to obtain a mechanism that allows the computation of independent subcontexts of a fuzzy context.

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Source: arXiv cs.AI Recent

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