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A New Text Clustering Method Using Hidden Markov Model

  • Yan Fu
  • Dongqing Yang
  • Shiwei Tang
  • Tengjiao Wang
  • Aiqiang Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4592)

Abstract

Being high-dimensional and relevant in semantics, text clustering is still an important topic in data mining. However, little work has been done to investigate attributes of clustering process, and previous studies just focused on characteristics of text itself. As a dynamic and sequential process, we aim to describe text clustering as state transitions for words or documents. Taking K-means clustering method as example, we try to parse the clustering process into several sequences. Based on research of sequential and temporal data clustering, we propose a new text clustering method using HMM(Hidden Markov Model). And through the experiments on Reuters-21578, the results show that this approach provides an accurate clustering partition, and achieves better performance rates compared with K-means algorithm.

Keywords

Hide Markov Model Cluster Process Dynamic Time Warping Vector Space Model Document Cluster 
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 2007

Authors and Affiliations

  • Yan Fu
    • 1
  • Dongqing Yang
    • 1
  • Shiwei Tang
    • 2
  • Tengjiao Wang
    • 1
  • Aiqiang Gao
    • 1
  1. 1.School of Electronics Engineering and Computer Science, Peking University, Beijing 100871China
  2. 2.National Laboratory on Machine Perception, Peking University, Beijing 100871China

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