Title: Clustering of imbalanced high-dimensional media data
Language: English
Authors: Brodinova, Sarka 
Zaharieva, Maia 
Filzmoser, Peter
Ortner, Thomas 
Breiteneder, Christian 
Category: Research Article
Issue Date: 2018
Journal: Advances in Data Analysis and Classification
ISSN: 1862-5347
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.
Keywords: Clustering; Imbalanced data; High-dimensional data; Media data; LOF
DOI: 10.1007/s11634-017-0292-z
Library ID: AC15320994
URN: urn:nbn:at:at-ubtuw:3-4789
Organisation: E105 - Institut für Stochastik und Wirtschaftsmathematik 
Publication Type: Article
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