Scalable Visual Analytics in {FCA}

 

Abstract

We adopt a visual analytic approach to FCA by combining computational analysis with interactive visualisation. Scaling FCA to the interactive analysis of large data sets poses four fundamental challenges: the time required to enumerate the vertices, arcs and labels of the lattice digraph; the difficulty of responsive presentation of, and meaningful user interaction with, a large digraph; the time required to enumerate (a basis for) all valid implications; and the discovery of insightful implications. This chapter briefly surveys potential solutions to these scalability challenges posed by big data volumes, and describes software prototypes and coordinated visualisations which explore some of them.

Citation

Please, cite this work as:

[Pat+22] T. Pattison, M. Enciso, Á. Mora, et al. “Scalable Visual Analytics in FCA”. In: Complex Data Analytics with Formal Concept Analysis. Ed. by R. Missaoui, L. Kwuida and T. Abdessalem. Springer International Publishing, 2022, pp. 167-200. DOI: 10.1007/978-3-030-93278-7_8. URL: https://doi.org/10.1007/978-3-030-93278-7_8.

BibTeX

@InCollection{Pattison2022,
     author = {Tim Pattison and Manuel Enciso and {’A}ngel Mora and Pablo Cordero and Derek Weber and Michael Broughton},
     booktitle = {Complex Data Analytics with Formal Concept Analysis},
     publisher = {Springer International Publishing},
     title = {Scalable Visual Analytics in {FCA}},
     year = {2022},
     editor = {Rokia Missaoui and L{’e}onard Kwuida and Talel Abdessalem},
     pages = {167–200},
     abstract = {We adopt a visual analytic approach to FCA by combining computational analysis with interactive visualisation. Scaling FCA to the interactive analysis of large data sets poses four fundamental challenges: the time required to enumerate the vertices, arcs and labels of the lattice digraph; the difficulty of responsive presentation of, and meaningful user interaction with, a large digraph; the time required to enumerate (a basis for) all valid implications; and the discovery of insightful implications. This chapter briefly surveys potential solutions to these scalability challenges posed by big data volumes, and describes software prototypes and coordinated visualisations which explore some of them.},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/books/sp/missaoui2022/PattisonEMCWB22.bib},
     doi = {10.1007/978-3-030-93278-7_8},
     timestamp = {Sun, 06 Oct 2024 01:00:00 +0200},
     url = {https://doi.org/10.1007/978-3-030-93278-7_8},
}

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Scalable Visual Analytics in {FCA}

Cites

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

[1] T. Pattison. “Spanning Concept Trees: Algorithms and Interaction”. In: Conceptual Knowledge Structures. Springer Nature Switzerland, 2024, p. 166–181. ISBN: 9783031678684. DOI: 10.1007/978-3-031-67868-4_12. URL: http://dx.doi.org/10.1007/978-3-031-67868-4_12.