The proliferation of documents, on both the Web and in private systems, makes knowledge discovery in document collections arduous. Clustering has been long recognized as a useful tool for the task. It groups like-items together, maximizing intra-cluster similarity and inter-cluster distance. Clustering can provide insight into the make-up of a document collection and is often used as the initial step in data analysis.

While most document clustering research to date has focused on moderate length single topic documents, real-life collections are often made up of very short or long documents. Short documents do not contain enough text to accurately compute similarities. Long documents often span multiple topics that general document similarity measures do not take into account. In this paper we will first give an overview of general purpose document clustering, and then focus on recent advancements in the next frontier in document clustering: long and short documents.