Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data

@article{Cesario2007TopDownPC,
  title={Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data},
  author={Eugenio Cesario and G. Manco and R. Ortale},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2007},
  volume={19},
  pages={1607-1624}
}
A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. The technique is based on a two-phase iterative procedure, which attempts to improve the overall quality of the whole partition. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. In the second phase, the number of clusters is fixed, and an attempt to optimize cluster assignments is done. On the… Expand
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