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Approximation Based on Representatives

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Rough Sets (IJCRS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11499))

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Abstract

In the authors’ previous research, a possible usage of the correlation clustering in rough set theory was investigated. Correlation clustering relies on a tolerance relation. Its output is a partition. The system of base sets can be derived from the partition and a new approximation space appears. This space focuses on the similarity (the tolerance relation) itself and it is different from the covering type approximation space relying on a tolerance relation. In real-world applications, the number of objects is very high. So it can be effective only if a portion of the data points is used. In this paper, a possible method is provided to choose the necessary number of objects that represent the data set. These members are called representatives and it can be useful to use them in the approximation of an arbitrary set.

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Acknowledgement

Supported by the ÚNKP-18-3 New National Excellence Program of the Ministry of Human Capacities.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Informatics, University of Debrecen, Egyetem tér 1, Debrecen, 4010, Hungary

    Dávid Nagy & László Aszalós

Authors
  1. Dávid Nagy
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  2. László Aszalós
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Corresponding author

Correspondence to Dávid Nagy .

Editor information

Editors and Affiliations

  1. University of Debrecen, Debrecen, Hungary

    Tamás Mihálydeák

  2. Southwest Petroleum University, Chengdu, China

    Fan Min

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Mohua Banerjee

  5. Fujian Normal University, Fuzhou, China

    Ivo Düntsch

  6. University of Rzeszów, Rzeszow, Poland

    Zbigniew Suraj

  7. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

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Nagy, D., Aszalós, L. (2019). Approximation Based on Representatives. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-22815-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22814-9

  • Online ISBN: 978-3-030-22815-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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