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dc.contributor.authorFowke, Michael
dc.contributor.authorHinze, Annika
dc.contributor.authorHeese, Ralf
dc.date.accessioned2014-01-29T03:44:05Z
dc.date.available2014-01-29T03:44:05Z
dc.date.issued2013-12
dc.identifier.citationFowke, M., Hinze, A., & Heese, R.(2013). Text categorization and similarity analysis: similarity measure, literature review. (Working paper 11/2013). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_NZ
dc.identifier.issn1177-777X
dc.identifier.urihttps://hdl.handle.net/10289/8432
dc.description.abstractDocument classification and provenance has become an important area of computer science as the amount of digital information is growing significantly. Organisations are storing documents on computers rather than in paper form. Software is now required that will show the similarities between documents (i.e. document classification) and to point out duplicates and possibly the history of each document (i.e. provenance). Poor organisation is common and leads to situations like above. There exists a number of software solutions in this area designed to make document organisation as simple as possible. I'm doing my project with Pingar who are a company based in Auckland who aim to help organise the growing amount of unstructured digital data. This reports analyses the existing literature in this area with the aim to determine what already exists and how my project will be different from existing solutions.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherUniversity of Waikato, Department of Computer Scienceen_NZ
dc.relation.ispartofseriesComputer Science Working Papersen_NZ
dc.rights© 2013 Michael Fowke, Annika Hinze, Ralf Heese.en_NZ
dc.subjectcomputer scienceen_NZ
dc.titleText categorization and similarity analysis: similarity measure, literature reviewen_NZ
dc.typeWorking Paperen_NZ
uow.relation.series11/2013en_NZ


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