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Clustering of imbalanced high-dimensional media data

Author

Listed:
  • Šárka Brodinová

    (TU Wien)

  • Maia Zaharieva

    (TU Wien
    University of Vienna)

  • Peter Filzmoser

    (TU Wien)

  • Thomas Ortner

    (University of Vienna
    TU Wien)

  • Christian Breiteneder

    (TU Wien)

Abstract

Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups differ so much from the remaining data that it may be worthy to look at them in more detail. In contrast, media with popular content appear in larger groups. Identifying groups of varying sizes is addressed by clustering of imbalanced data. Clustering highly imbalanced media groups is additionally challenged by the high dimensionality of the underlying features. In this paper, we present the imbalanced clustering (IClust) algorithm designed to reveal group structures in high-dimensional media data. IClust employs an existing clustering method in order to find an initial set of a large number of potentially highly pure clusters which are then successively merged. The main advantage of IClust is that the number of clusters does not have to be pre-specified and that no specific assumptions about the cluster or data characteristics need to be made. Experiments on real-world media data demonstrate that in comparison to existing methods, IClust is able to better identify media groups, especially groups of small sizes.

Suggested Citation

  • Šárka Brodinová & Maia Zaharieva & Peter Filzmoser & Thomas Ortner & Christian Breiteneder, 2018. "Clustering of imbalanced high-dimensional media data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 261-284, June.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:2:d:10.1007_s11634-017-0292-z
    DOI: 10.1007/s11634-017-0292-z
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    References listed on IDEAS

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    1. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
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