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Instance Weighted Clustering: Local Outlier Factor and K-Means

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

Clustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and entropy information. However, little research has made use of instance density information to weight instances. In this paper we use density to define instance weights. We propose two novel instance weighted clustering algorithms based on Local Outlier Factor and compare them against plain k-means and traditional instance selection.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Local_outlier_factor.

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Authors and Affiliations

  1. The University of Hertfordshire, Hatfield, UK

    Paul Moggridge, Na Helian, Yi Sun, Mariana Lilley & Vito Veneziano

Authors
  1. Paul Moggridge
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  2. Na Helian
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  3. Yi Sun
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  4. Mariana Lilley
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  5. Vito Veneziano
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Corresponding author

Correspondence to Paul Moggridge .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Moggridge, P., Helian, N., Sun, Y., Lilley, M., Veneziano, V. (2020). Instance Weighted Clustering: Local Outlier Factor and K-Means. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_34

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

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