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GrFCM – Granular Clustering of Granular Data

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Man-Machine Interactions 6 (ICMMI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1061 ))

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Abstract

Granular computing is a new paradigm in data mining. It mimics a procedure commonly used by humans. A data granule may be defined as a collection of related entities in sense of similarity, proximity, indiscernibility. Nowadays granular computing focuses on elaboration of granules from data. This step in granular computing is well researched. Our objective is the next step: we would like to focus on computing with granules. In the paper we propose a new clustering algorithm that works with granules instead of numbers. The algorithm takes a collection of granules as an input and clusters them into output granules.

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Acknowledgements

The research has been supported by the Rector’s Grant for Research and Development (Silesian University of Technology, grant number: 02/020/RGJ19/0165).

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Krzysztof Siminski

Authors
  1. Krzysztof Siminski
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Correspondence to Krzysztof Siminski .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Sebastian Deorowicz

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Katarzyna Harężlak

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Piotrowska

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Siminski, K. (2020). GrFCM – Granular Clustering of Granular Data. In: Gruca, A., Czachórski, T., Deorowicz, S., Harężlak, K., Piotrowska, A. (eds) Man-Machine Interactions 6. ICMMI 2019. Advances in Intelligent Systems and Computing, vol 1061 . Springer, Cham. https://doi.org/10.1007/978-3-030-31964-9_11

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