Text Mining pp 249-268 | Cite as

Text Clustering: Evaluation

  • Taeho Jo
Part of the Studies in Big Data book series (SBD, volume 45)


This chapter is concerned with the schemes of evaluating text clustering systems or approaches.


  1. 7.
    Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suha, E., Doughertya, E.R.: Model-based evaluation of clustering validation measures. Pattern Recogn. 40, 807–824 (2007)CrossRefGoogle Scholar
  2. 21.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001)Google Scholar
  3. 25.
    Jo, T.: The Implementation of Dynamic Document Organization Using the Integration of Text Clustering and Text Categorization, University of Ottawa (2006)Google Scholar
  4. 27.
    Jo, T.: Inverted Index based modified version of KNN for text categorization. J. Inf. Process. Syst. 4, 17–26 (2008)CrossRefGoogle Scholar
  5. 28.
    Jo, T.: Neural text categorizer for exclusive text categorization. J. Inf. Process. Syst. 4, 77–86 (2008)CrossRefGoogle Scholar
  6. 44.
    Jo, T., Lee, M.: The evaluation measure of text clustering for the variable number of clusters. Lect. Notes Comput. Sci. 4492, 871–879 (2007)CrossRefGoogle Scholar
  7. 85.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)CrossRefGoogle Scholar
  8. 94.
    Vendramin, L., Campello, R., Hruschka E.R.: On the comparison of relative clustering validity criteria. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 733–744 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Taeho Jo
    • 1
  1. 1.School of Game, Hongik UniversitySeoulKorea (Republic of)

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