Centroid-Based Actionable 3D Subspace Clustering

@article{Sim2013CentroidBasedA3,
  title={Centroid-Based Actionable 3D Subspace Clustering},
  author={Kelvin Sim and Ghim-Eng Yap and David R. Hardoon and Vivekanand Gopalkrishnan and Gao Cong and Suryani Lukman},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2013},
  volume={25},
  pages={1213-1226}
}
Actionable 3D subspace clustering from real-world continuous-valued 3D (i.e., object-attribute-context) data promises tangible benefits such as discovery of biologically significant protein residues and profitable stocks, but existing algorithms are inadequate in solving this clustering problem; most of them are not actionable (ability to suggest profitable or beneficial actions to users), do not allow incorporation of domain knowledge, and are parameter sensitive, i.e., the wrong threshold… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 45 references

Numerical Optimization, pages 497– 528

  • J. Nocedal, S. J. Wright
  • Springer,
  • 2006
Highly Influential
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