High-dimensional datasets often contain multiple meaningful clusterings in
different subspaces. For example, objects can be clustered either by color,
weight, or size, revealing different interpretations of the given dataset. A
variety of approaches are able to identify such non-redundant clusterings.
However, most of these methods require the user to specify the expected number
of subspaces and clusters for each subspace. Stating these values is a
non-trivial problem and usually requires detailed knowledge of the input
dataset. In this paper, we propose a framework that utilizes the Minimum
Description Length Principle (MDL) to detect the number of subspaces and
clusters per subspace automatically. We describe an efficient procedure that
greedily searches the parameter space by splitting and merging subspaces and
clusters within subspaces. Additionally, an encoding strategy is introduced
that allows us to detect outliers in each subspace. Extensive experiments show
that our approach is highly competitive to state-of-the-art methods.

该论文提出了一个利用最小描述长度原则（MDL）自动检测子空间数量和每个子空间中的簇数的框架，并描述了一种有效的过程，通过在子空间内进行分裂和合并子空间和簇来贪婪搜索参数空间。此外，介绍了一种编码策略，可在每个子空间中检测异常值。广泛的实验表明，该方法与最先进的方法具有很高的竞争力。