Docclustering: An Implementation of the Novel Ps-Document Clustering Algorithm a Preprint

A BSTRACT Document clustering is widely used in science for data retrieval and organisation. DocClustering is developed to include and use a novel algorithm called PS-Document Clustering that has been first introduced in 2017. This method combines approaches of graph theory with state of the art NLP-technologies. This new heuristic has been shown to be superior to conventional algorithms and it provides – given a suiting similarity measure – a more accurate clustering on biological and medical data. Since the application is written for research on biomedical literature, interfaces for PubMed and SCAIView are available. In this brief report the source code as well as a short overview about the new features, novel heuristics and approaches are provided. The software can be obtained from the authors or directly downloaded from GitHub, see https://github.com/jd-s/DocClustering .