Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/48627
Title: Relation and fuzzy clustering for document categorization and analysis
Authors: Mei, Jian-Ping
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2012
Source: Mei, J.-P. (2012). Relation and fuzzy clustering for document categorization and analysis. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
URI: https://hdl.handle.net/10356/48627
DOI: 10.32657/10356/48627
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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