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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Data Mining of Text Documents
verfasst von : Evangelos Triantaphyllou
Erschienen in: Data Mining and Knowledge Discovery via Logic-Based Methods
Verlag: Springer US
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This chapter investigates the problem of classifying
sub text documents (mining of)
text documents into two disjoint classes. It does so by employing a data mining approach based on the OCAT algorithm. This chapter is based on the work discussed in [
aut Nieto Sanchez, S.
Nieto Sanchez,
aut Triantaphyllou, E.
Triantaphyllou, and Kraft, 2002]. In the present setting two
sub sample set
sample sets of training examples (text documents) are assumed to be available. An approach is developed that uses
sub indexing terms, see
keywords
indexing terms to form patterns of logical expressions (Boolean functions) that next are used to classify new text documents (which are of unknown class). This is a typical case of
sub supervised classification
supervised “crisp” classification.