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A Comparative Study on Text Clustering Methods

  • Yan Zheng
  • Xiaochun Cheng
  • Ronghuai Huang
  • Yi Man
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

Text clustering is one of the most important research areas in text mining, which handles the text automatically to discover implicit knowledge. It groups text into different clusters by contents without apriori knowledge. In this paper, different text clustering methods are studied and three text clustering validation criteria are studied and used to evaluate the experimental results. We compare and contrast the effectiveness of k-means and FIHC text clustering methods by experiments, and address the different levels of quality of the resulting text clusters.

Keywords

Recall Rate Text Cluster Division Function Feedback Neural Network Apriori Knowledge 
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 Berlin Heidelberg 2006

Authors and Affiliations

  • Yan Zheng
    • 1
    • 2
  • Xiaochun Cheng
    • 2
    • 3
  • Ronghuai Huang
    • 2
  • Yi Man
    • 1
  1. 1.School of Computer Science and TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Knowledge Science and Engineering InstituteBeijing Normal UniversityBeijingChina
  3. 3.Middlesex UniversityUK

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