Clustering nonlinearly separable and unbalanced data set

@article{Yang2004ClusteringNS,
  title={Clustering nonlinearly separable and unbalanced data set},
  author={XuLei Yang and Qing Hai Song and Aize Cao},
  journal={2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791)},
  year={2004},
  volume={2},
  pages={491-496 Vol.2}
}
In this paper, a new clustering method, kernel based deterministic annealing (KBDA) algorithm, is developed. This development provides a possible solution for the nonlinearly separable and unbalanced data clustering problems. Basically, the kernel based method makes nonlinearly separable data set more likely linearly separable through a nonlinear data transformation from input space into a high dimensional feature space. Furthermore, the mass possibilities of different clusters are incorporated… CONTINUE READING