A set theory based similarity measure for text clustering and classification
A set theory based similarity measure for text clustering and classification
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Publisher
Cham: Springer International Publishing
Journal title
Language
English
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Publisher
Cham: Springer International Publishing
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Scope and Contents
Contents
Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single me...
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Full title
A set theory based similarity measure for text clustering and classification
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_dad798d9e45f466a8c87a1f6f97a8751
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_dad798d9e45f466a8c87a1f6f97a8751
Other Identifiers
ISSN
2196-1115
E-ISSN
2196-1115
DOI
10.1186/s40537-020-00344-3
