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

Mohamed-Rafik Bouguelia (1) , Yolande Belaïd (1) , Abdel Belaïd (1)

1 READ - Recognition of writing and analysis of documents

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.

Mots clés: Data Streams; Online Learning; Unsupervised Neural Clustering; Incremental Learning

Domaines: Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]; Réseau de neurones [cs.NE]

Mohamed-Rafik Bouguelia, Yolande Belaïd, Abdel Belaïd. An Adaptive Incremental Clustering Method Based on the Growing Neural Gas Algorithm. 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM 2013, Feb 2013, Barcelona, Spain. pp.42-49, ⟨10.5220/0004256600420049⟩. ⟨hal-00794354⟩