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Clustering Text Data Streams

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Abstract

Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and only use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality. Recently, researchers argue that semantic smoothing model is more efficient than the existing TF*IDF scheme for improving text clustering quality. However, the existing semantic smoothing model is not suitable for dynamic text data context. In this paper, we extend the semantic smoothing model into text data streams context firstly. Based on the extended model, we then present two online clustering algorithms OCTS and OCTSM for the clustering of massive text data streams. In both algorithms, we also present a new cluster statistics structure named cluster profile which can capture the semantics of text data streams dynamically and at the same time speed up the clustering process. Some efficient implementations for our algorithms are also given. Finally, we present a series of experimental results illustrating the effectiveness of our technique.

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Authors and Affiliations

  1. Department of Computer Science, Sun Yat-Sen University, Guangzhou, 510275, China

    Yu-Bao Liu, Jia-Rong Cai & Jian Yin

  2. Department of Computer Science and Engineering, the Chinese University of Hong Kong, Hong Kong, China

    Ada Wai-Chee Fu

Authors
  1. Yu-Bao Liu
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  2. Jia-Rong Cai
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  3. Jian Yin
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  4. Ada Wai-Chee Fu
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Corresponding author

Correspondence to Yu-Bao Liu.

Additional information

Supported by the National Natural Science Foundation of China under Grant Nos. 60573097, 60703111, 60773198, the Natural Science Foundation of Guangdong Province under Grant No. 06104916, the Specialized Research Foundation for the Doctoral Program of Higher Education under Grant No. 20050558017 and the Program for New Century Excellent Talents in University of China under Grant No. NCET–06–0727.

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Liu, YB., Cai, JR., Yin, J. et al. Clustering Text Data Streams. J. Comput. Sci. Technol. 23, 112–128 (2008). https://doi.org/10.1007/s11390-008-9115-1

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  • DOI: https://doi.org/10.1007/s11390-008-9115-1

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