We discuss the use of ensemble techniques for unsupervised learning, with a focus on outlier detection.

To introduce the field, we will briefly sketch the data mining task of unsupervised outlier detection and discuss some basic considerations about ensemble techniques. Then we give an overview on existing approaches to using ensemble techniques for outlier detection as well as on the challenges in doing so. Some of our recent contributions to this field will be discussed in more detail, highlighting the issue of diversity of models in building effective ensembles. Finally, we will return to the broader perspective and reason about the application of ensemble techniques in the context of unsupervised learning in general.