This thesis focuses on the investigations of using fuzzy clustering for automatic document categorization based on relations between document and other types of objects. Three approaches called Fk-Parts, LinkFCM and FC-MR are proposed to handle the document clustering problem under different scenarios. We start with a basic situation, and propose Fk-Parts to cluster documents based on document-document relation. The new mechanism of using multiple weighted medoids to represent each cluster makes Fk-Parts perform better than single medoid based approaches. After that, we consider situations where both vector representation of documents and document-document relation are available. LinkFCM is then formulated by incorporating relation into the well known fuzzy c-means approach, so that both types of data are considered in clustering. Finally we propose a fuzzy approach of multi-type relational data clustering FC-MR. This approach simultaneously clusters documents and other types of objects based on the relations among them.