Density Conscious Subspace Clustering for High-Dimensional Data

@article{Chu2010DensityCS,
  title={Density Conscious Subspace Clustering for High-Dimensional Data},
  author={Yi-Hong Chu and Jen-Wei Huang and Kun-Ta Chuang and De-Nian Yang and Ming-Syan Chen},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2010},
  volume={22},
  pages={16-30}
}
Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, the identification of dense regions in previous works lacks of considering a critical problem, called "the density divergence problemrdquo in this paper, which refers to the phenomenon that the region… CONTINUE READING
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Emerging Pattern Based Subspace Clustering of Microarray Gene Expression Data Using Mixture Models

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