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An Analysis of Constructed Categories for Textual Classification Using Fuzzy Similarity and Agglomerative Hierarchical Methods

  • Marcus V. C. GuelpeliEmail author
  • Ana Cristina Bicharra Garcia
  • Flavia Cristina Bernardini
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Ambiguity is a challenge faced by systems that handle natural language. To assuage the issue of linguistic ambiguities found in text classification, this work proposes a text categorizer using the methodology of Fuzzy Similarity. The clustering algorithms Stars and Cliques are adopted in the Agglomerative Hierarchical method and they identify the groups of texts by specifying some type of relationship rule to create categories based on the similarity analysis of the textual terms. The proposal is based on the methodology suggested, categories can be created from the analysis of the degree of similarity of the texts to be classified, without needing to determine the number of initial categories. The combination of techniques proposed in the categorizer’s steps brought satisfactory results, proving to be efficient in textual classification.

Keywords

Fuzzy Logic Text Mining Stop Criterion Source Text Hierarchical Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  • Marcus V. C. Guelpeli
    • 1
    Email author
  • Ana Cristina Bicharra Garcia
    • 1
  • Flavia Cristina Bernardini
    • 2
  1. 1.Departamento de Ciência da Computação, Instituto de Computação—ICUniversidade Federal Fluminense—UFFSão DomingosBrazil
  2. 2.Departamento de Ciência e Tecnologia—RCT, Pólo Universitário de Rio das Ostras—PUROUniversidade Federal Fluminense—UFFRio das OstrasBrazil

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