  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.
