Abstract
BackgroundIn text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation.ResultsIn this paper we present and discuss a novel graph-theoretical approach for document clustering and its application on a real-world data set. We will show that the well-known graph partition to stable sets or cliques can be generalized to pseudostable sets or pseudocliques. This allows to perform a soft clustering as well as a hard clustering. The software is freely available on GitHub.ConclusionsThe presented integer linear programming as well as the greedy approach for this mathcal {NP}-complete problem lead to valuable results on random instances and some real-world data for different similarity measures. We could show that PS-Document Clustering is a remarkable approach to document clustering and opens the complete toolbox of graph theory to this field.
Highlights
In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics
For the clustering use case, we study MEDLINE abstracts and associated metadata that are processed by ProMiner, a named entity recognition system ([38]), and indexed by the semantic information retrieval platform SCAIView ([39])
We have shown a novel approach for document clustering considering hard clustering as well as soft clustering
Summary
Document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. Soft Document Clustering using a graph partition in multiple pseudostable sets has been introduced in [1]. We would like to extend this approach by making some fundamental theoretical additions, discuss the correct calculation of the bounds and ι and discuss some output data. Document Clustering ( known as Text Clustering) is a specific application of textmining and a sub-problem of cluster analyses. The approach discussed in this paper can be applied to other clustering subjects, but the purpose of text clustering is the most common. The application of Document Clustering is a wide and open field and in terms of complexity it is still under heavy research, see [2, 65ff] and [3, 47]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
