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      Relation and fuzzy clustering for document categorization and analysis.

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      Author
      Mei, Jian-Ping.
      Date of Issue
      2012
      School
      School of Electrical and Electronic Engineering
      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.
      Subject
      DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
      DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
      Type
      Thesis
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      • EEE Theses (Open Access)

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