A New Simplified Gravitational Clustering Method for Multi-prototype Learning Based on Minimum Classification Error Training

  • Teng Long
  • Lian-Wen Jin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)


In this paper, we propose a new simplified gravitational clustering method for multi-prototype learning based on minimum classification error (MCE) training. It simulates the process of the attraction and merging of objects due to their gravity force. The procedure is simplified by not considering velocity and multi-force attraction. The proposed hierarchical method does not depend on random initialization and the results can be used as better initial centers for K-means to achieve higher performance under the SSE (sum-squared-error) criterion. The experimental results on the recognition of handwritten Chinese characters show that the proposed approach can generate better prototypes than K-means and the results obtained by MCE training can be further improved when the proposed method is employed.


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  1. 1.
    Liu, C.-L., Jaeger, S., Nakagawa, M.: Online Recognition of Chinese Characters: The State-of-the-Art. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(2), 198–213 (2004)CrossRefGoogle Scholar
  2. 2.
    Rahman, A.F.R., Fairhurst, M.C.: Multi-prototype Classification: Improved Modeling of the Variability of Handwritten Data using Statistical Clustering Algorithms. Electronics Letters 33(14), 1208–1210 (1997)CrossRefGoogle Scholar
  3. 3.
    Kanungo, T., et al.: An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 881–892 (2002)CrossRefGoogle Scholar
  4. 4.
    Kohonen, T.: The Self-Organizing Map. IEEE Proceedings 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  5. 5.
    Juang, B.-H., Katagiri, S.: Discriminative Learning for Minimum Error Classification. IEEE Trans. on Signal Processing 40, 3043–3054 (1992)zbMATHCrossRefGoogle Scholar
  6. 6.
    Huo, Q., Ge, Y., Feng, Z.-D.: High Performance Chinese OCR Based on Gabor Features, Discriminative Feature Extraction and Model Training. In: Proc. ICASSP 2001, vol. 3, pp. 1517–1520 (2001)Google Scholar
  7. 7.
    Katagiri, S., Juang, B.-H., Lee, C.-H.: Pattern Recognition Using a Family of Design Algorithms Based Upon the Generalized Probabilistic Descent Method. IEEE Proceedings 86(11), 2345–2373 (1998)CrossRefGoogle Scholar
  8. 8.
    Chena, C.-Y., Hwanga, S.-C., Oyanga, Y.-J.: A statistics-based approach to control the quality of subclusters in incremental gravitational clustering. Pattern Recognition 38, 2256–2269 (2005)CrossRefGoogle Scholar
  9. 9.
    Wang, Q., et al.: Match between Normalization Schemes and Feature Sets for Handwritten Chinese Character Recognition. In: Proc. ICDAR 2001, pp. 551–555 (2001)Google Scholar
  10. 10.
    Wright, W.E.: Gravitational Clustering. Pattern Recognition 9, 1149–1160 (1997)Google Scholar
  11. 11.
    Berkhin, P.: Survey of Clustering Data Mining Techniques. Technical Report, Accrue. Software Inc., San Jose, CA, USA (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Teng Long
    • 1
  • Lian-Wen Jin
    • 1
  1. 1.Department of Electronic and Communication EngineeringSouth China University of TechnologyGuangzhouChina

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