Incremental Neural Gas Algorithm

An Incremental Neural Gas Algorithm (IGNG) is a Neural Clustering Algorithm based on Incremental Learning.

• Context:
  • It was first published by Lamirel et al. (2010).
  • …
• Example(s):
  • …
• Counter-Example(s):
  • A Self-Organizing Map (SOM),
  • A Neural Gas Algorithm (NG),
  • A Growing Neural Gas Algorithm(GNG).
• See: Machine Learning, Incremental Learning System, Neural Clustering.

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References

• (Lamirel et al., 2010) ⇒ Lamirel, J. C., Boulila, Z., Ghribi, M., Cuxac, P., & François, C. (2010, October). "A new incremental neural clustering approach for performing reliable large scope scientometrics analysis". In 6th International Conference on Webometrics, Informetrics and Scientometrics and 11th COLLNET Meeting.
  • ABSTRACT: Neural clustering algorithms show high performance in the usual context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental neural gas algorithm (IGNG). Nevertheless, this paper highlights clearly the drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. A new incremental growing neural gas algorithm exploiting knowledge issued from clusters current labeling in an incremental way is proposed as an alternative to the original distance based algorithm. This solution leads to obtain very significant increase of performance for the clustering of heterogeneous textual data. Moreover, it provides a real incremental character to the proposed algorithm.

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