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International Conference on Conceptual Structures

ICCS 2019: Graph-Based Representation and Reasoning pp 57–71Cite as

Formal Context Generation Using Dirichlet Distributions

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11530))

Abstract

Randomly generating formal contexts is an important task in the realm of formal concept analysis, in particular when comparing the performance of algorithms. We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant method, coin-tossing, recapitulate some of its shortcomings and examine its stochastic model. Building upon this we propose our Dirichlet model and develop an algorithm employing this idea. Through an experimental evaluation we show that our approach is a significant improvement with respect to the variety of contexts generated.

Authors are given in alphabetical order. No priority in authorship is implied.

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Notes

  1. 1.

    https://github.com/exot/conexp-clj.

  2. 2.

    https://github.com/maximilian-felde/formal-context-generator.

References

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Author information

Authors and Affiliations

  1. Knowledge and Data Engineering Group, University of Kassel, Kassel, Germany

    Maximilian Felde & Tom Hanika

  2. Interdisciplinary Research Center for Information System Design, University of Kassel, Kassel, Germany

    Maximilian Felde & Tom Hanika

Authors
  1. Maximilian Felde
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  2. Tom Hanika
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Corresponding author

Correspondence to Maximilian Felde .

Editor information

Editors and Affiliations

  1. Philipps-Universität Marburg, Marburg, Germany

    Dominik Endres

  2. FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Eggenstein-Leopoldshafen, Germany

    Mehwish Alam

  3. Babes-Bolyai University, Cluj-Napoca, Romania

    Diana Şotropa

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Felde, M., Hanika, T. (2019). Formal Context Generation Using Dirichlet Distributions. In: Endres, D., Alam, M., Şotropa, D. (eds) Graph-Based Representation and Reasoning. ICCS 2019. Lecture Notes in Computer Science(), vol 11530. Springer, Cham. https://doi.org/10.1007/978-3-030-23182-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-23182-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23181-1

  • Online ISBN: 978-3-030-23182-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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