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Similarity reasoning in formal concept analysis: from one- to many-valued contexts

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Abstract

In this paper, concept similarity in formal concept analysis (FCA) with many-valued contexts is addressed. In particular, this work focuses on FCA many-valued contexts where attribute values are intervals (FCA with interordinal scaling), here referred to as IFCA. IFCA is based on interval type-2 fuzzy sets, which provide a simplification of the more general type-2 fuzzy sets. In this work, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated in the literature, although the increasing interest in the combination of FCA with fuzzy sets. Note that the topic addressed in this paper is presented by providing simple examples in order to reach a broad audience of readers.

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  1. Istituto di Analisi dei Sistemi ed Informatica (IASI) “Antonio Ruberti”, National Research Council, Via dei Taurini 19, 00185, Rome, Italy

    Anna Formica

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Formica, A. Similarity reasoning in formal concept analysis: from one- to many-valued contexts. Knowl Inf Syst 60, 715–739 (2019). https://doi.org/10.1007/s10115-018-1252-4

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