Clustering of imbalanced high-dimensional media data

TL;DR

The paper introduces IClust, a novel clustering algorithm tailored for high-dimensional, imbalanced media data that effectively identifies small and large groups without predefining the number of clusters.

Contribution

IClust is a new clustering method that automatically determines the number of clusters and handles high-dimensional, imbalanced media data effectively.

Findings

IClust outperforms existing methods in identifying small media groups.

It does not require pre-specifying the number of clusters.

It works well with high-dimensional media features.

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.