An Adaptive Incremental Clustering Method based on the Growing Neural Gas Algorithm

Mohamed-Rafik Bouguelia, Yolande Belaïd, Abdel Belaïd

2013

Abstract

Usually, incremental algorithms for data streams clustering not only suffer from sensitive initialization parameters, but also incorrectly represent large classes by many cluster representatives, which leads to decrease the computational efficiency over time. We propose in this paper an incremental clustering algorithm based on ”growing neural gas” (GNG), which addresses this issue by using a parameter-free adaptive threshold to produce representatives and a distance-based probabilistic criterion to eventually condense them. Experiments show that the proposed algorithm is competitive with existing algorithms of the same family, while maintaining fewer representatives and being independent of sensitive parameters.

References

  1. Aggarwal, C. C., Han, J., Wang, J., and Yu, P. S. (2003). A framework for clustering evolving data streams. Proceedings of the 29th international conference on Very large data bases, pages 81-92.
  2. Chen, Y. and Tu, L. (2007). Density-based clustering for real-time stream data. International Conference on Knowledge Discovery and Data Mining, pages 133- 142.
  3. Frank, A. and Asuncion, A. (2010). The uci machine learning repository. http://archive.ics.uci.edu/ml/.
  4. Fritzke, B. (1995). A growing neural gas network learns topologies. Neural Information Processing Systems, pages 625-632.
  5. Hamza, H., Belaid, Y., Belaid, A., and Chaudhuri, B. (2008). Incremental classification of invoice documents. International Conference on Pattern Recognition, pages 1-4.
  6. Keogh, E., Lonardi, S., and Ratanamahatana, C. A. (2004). Towards parameter-free data mining. Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, pages 206-215.
  7. LeCun Yann, C. C. (2010). MNIST handwritten digit database. http://yann.lecun.com/exdb/mnist/.
  8. Martinetz, T. (1993). Competitive hebbian learning rule forms perfectly topology preserving maps. ICANN, pages 427-434.
  9. O'Callaghan, L., Meyerson, A., Motwani, R., Mishra, N., and Guha, S. (2002). Streaming-data algorithms for high-quality clustering. In ICDE'02, pages 685-685.
  10. Prudent, Y. and Ennaji, A. (2005). An incremental growing neural gas learns topology. European Symposium on Artificial Neural Networks.
  11. Rosenberg, A. and Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. NIPS, pages 410-420.
  12. Shen, F., Ogura, T., and Hasegawa, O. (2007). An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks, pages 893- 903.
  13. Shindler, M., Wong, A., and Meyerson, A. (2011). Fast and accurate k-means for large datasets. NIPS, pages 2375-2383.
Download


Paper Citation


in Harvard Style

Bouguelia M., Belaïd Y. and Belaïd A. (2013). An Adaptive Incremental Clustering Method based on the Growing Neural Gas Algorithm . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 42-49. DOI: 10.5220/0004256600420049


in Bibtex Style

@conference{icpram13,
author={Mohamed-Rafik Bouguelia and Yolande Belaïd and Abdel Belaïd},
title={An Adaptive Incremental Clustering Method based on the Growing Neural Gas Algorithm},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={42-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004256600420049},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Adaptive Incremental Clustering Method based on the Growing Neural Gas Algorithm
SN - 978-989-8565-41-9
AU - Bouguelia M.
AU - Belaïd Y.
AU - Belaïd A.
PY - 2013
SP - 42
EP - 49
DO - 10.5220/0004256600420049