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Document Clustering by Relevant Terms: An Approach

  • Cecilia Reyes-Peña
  • Mireya Tovar VidalEmail author
  • José de Jesús Lavalle Martínez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)

Abstract

In this work, a document clustering based on relevant terms into an untagged medical text corpus approach is presented. To achieve this, to create a list of documents containing each word is necessary. Then, for relevant term extraction, the frequency of each term is obtained in order to compute the word weight into the corpus and into each document. Finally, the clusters are built by mapping using main concepts from an ontology and the relevant terms (only subjects), assuming that if two words appear in the same documents these words are related. The obtained clusters have a category corresponding to ontology concepts, and they are measured with cluster from K-Means (assuming the k-Means cluster were well formed) using the Overlap Coefficient and obtaining 70% of similarity among the clusters.

Keywords

Documents clustering Relevant terms Medical corpus 

Notes

Acknowledgment

This work is supported by the Sectoral Research Fund for Education with the CONACyT project 257357, and partially supported by the VIEP-BUAP project.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Cecilia Reyes-Peña
    • 1
  • Mireya Tovar Vidal
    • 1
    Email author
  • José de Jesús Lavalle Martínez
    • 1
  1. 1.Faculty of Computer ScienceBenemérita Universidad Autónoma de PueblaPueblaMexico

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