Concept lattices are central to Formal Concept Analysis (FCA), offering a structured and interpretable way to represent and analyze relationships between objects and attributes. However, producing clear and interactive lattice visualizations remains challenging, especially for larger or more complex datasets. Existing FCA tools often lack interactive features or rely on outdated technologies, limiting their accessibility and usability. In this paper, we introduce lattice.js, a lightweight JavaScript library designed for the interactive visualization of concept lattices. Built on top of the D3.js framework, lattice.js supports hierarchical layout computation using a heuristic adaptation of the Coffman–Graham algorithm combined with barycentric reordering to improve readability. The library also offers interactive exploration features such as zooming, filtering, node selection, and reduced labeling strategies. Additionally, it provides structural metric computation and supports exporting visualizations in multiple formats. We demonstrate the library’s functionality using a well-known FCA example, highlighting its accessibility and practical value for FCA applications.

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Interactive Exploration of Concept Lattices with lattice.js: A Web-Based Visualization Library

  • Fabiola Hodo,
  • Sara Balderas-Díaz,
  • Gabriel Guerrero-Contreras,
  • Barış Sertkaya

摘要

Concept lattices are central to Formal Concept Analysis (FCA), offering a structured and interpretable way to represent and analyze relationships between objects and attributes. However, producing clear and interactive lattice visualizations remains challenging, especially for larger or more complex datasets. Existing FCA tools often lack interactive features or rely on outdated technologies, limiting their accessibility and usability. In this paper, we introduce lattice.js, a lightweight JavaScript library designed for the interactive visualization of concept lattices. Built on top of the D3.js framework, lattice.js supports hierarchical layout computation using a heuristic adaptation of the Coffman–Graham algorithm combined with barycentric reordering to improve readability. The library also offers interactive exploration features such as zooming, filtering, node selection, and reduced labeling strategies. Additionally, it provides structural metric computation and supports exporting visualizations in multiple formats. We demonstrate the library’s functionality using a well-known FCA example, highlighting its accessibility and practical value for FCA applications.