A high-performance multithreaded approach for clustering a stream of documents

We present an efficient approach for clustering a massive stream of textual documents, with particular emphasis on parallelization by the means of multithreaded processing. The underlying clustering algorithm is adaptable to changes in the stream and includes the ability to split and merge clusters, as well as to discard old data.