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Covering Concept Lattices with Concept Chains

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Graph-Based Representation and Reasoning (ICCS 2019)

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

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Abstract

The total number of concepts in a concept lattice tends to grow exponentially with the size of a context. There are numerous methods for selecting a subset of concepts based on some interestingness measure. We propose a method for finding interesting concept chains instead of interesting concepts. Concept chains also correspond to a certain visual rearrangement of a binary data table called a seriation. In a case study on the performance data of 852 students 80% of the corresponding formal context was covered by a single concept chain. We present three heuristic algorithms (MS-Chain, FL-Sort, KM-chain) for finding the concept chain cover in an efficient manner.

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Acknowledgements

We would like to thank Marko Kääramees for helping to provide the student data and UCI Machine Learning Repository for providing the datasets used for algorithm comparison.

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Authors and Affiliations

  1. Software Science Department, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Ants Torim, Marko Mets & Kristo Raun

Authors
  1. Ants Torim
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  2. Marko Mets
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  3. Kristo Raun
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Corresponding author

Correspondence to Ants Torim .

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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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Torim, A., Mets, M., Raun, K. (2019). Covering Concept Lattices with Concept Chains. 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_14

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  • DOI: https://doi.org/10.1007/978-3-030-23182-8_14

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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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