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A concept lattice provides a model of a dataset that can be navigated and explored by an analyst in an interactive way, except when the concept lattice is too large. Such a problem can be overcome by building a representation of the whole concept lattice that keeps a reasonable size and that can be interpreted by the analyst. Relying on previous work about link key discovery, we revisit in this paper an approach based on Formal Concept Analysis (FCA) and Agglomerative Hierarchical Clustering (AHC) applied to a set of concepts for building a representative set of clusters. Accordingly, we propose an AHC algorithm that (a) efficiently computes this representative set, and (b) respects the ordinal structure of the original concept lattice. A set of experiments performed over real datasets shows the effectiveness of our approach.

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A Proposal for Building a Compact and Tunable Representation of a Concept Lattice Based on Clustering

  • 会议论文

  • 发表日期：

    2025年

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  • Jaume Baixeries,
  • Alexandre Bazin,
  • Jérôme David,
  • Amedeo Napoli

摘要

A concept lattice provides a model of a dataset that can be navigated and explored by an analyst in an interactive way, except when the concept lattice is too large. Such a problem can be overcome by building a representation of the whole concept lattice that keeps a reasonable size and that can be interpreted by the analyst. Relying on previous work about link key discovery, we revisit in this paper an approach based on Formal Concept Analysis (FCA) and Agglomerative Hierarchical Clustering (AHC) applied to a set of concepts for building a representative set of clusters. Accordingly, we propose an AHC algorithm that (a) efficiently computes this representative set, and (b) respects the ordinal structure of the original concept lattice. A set of experiments performed over real datasets shows the effectiveness of our approach.