Data Mining of Text Documents

Evangelos Triantaphyllou ()
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Evangelos Triantaphyllou: Louisiana State University

Chapter Chapter 13 in Data Mining and Knowledge Discovery via Logic-Based Methods, 2010, pp 257-276 from Springer

Abstract: Abstract 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.

Keywords: Cross Validation; Boolean Function; Wall Street Journal; Text Document; Vector Space Model (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4419-1630-3_13

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DOI: 10.1007/978-1-4419-1630-3_13

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