Clustering on High Dimensional Data that Reduces Dimensionality Using Dimension Reduction Techniques

@inproceedings{GOWRILAKSSHMI2011ClusteringOH,
  title={Clustering on High Dimensional Data that Reduces Dimensionality Using Dimension Reduction Techniques},
  author={K. S. GOWRILAKSSHMI},
  year={2011}
}
  • K. S. GOWRILAKSSHMI
  • Published 2011
Dimensionality reduction is the search of small set of features to describe a large set of observed dimensions. The purpose of dimensionality reduction is to transform a high dimensional data set in to low dimensional space using clustering techniques of k-means. Recently new non linear methods introduced for reducing the dimensionality data called Locally Linear Embedding (LLE).LLE combined with K-means clustering in to coherent frame work to adaptively select the most discriminant subspace. K… CONTINUE READING
1 Citations
31 References
Similar Papers

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 31 references

2008)‖Hessian eigen maps: Locally Linear techniques for high dimensional data

  • Wolfe P.J, .A Belabbas
  • 2008
2 Excerpts

Hessian eigen maps : Locally Linear techniques for high dimensional data

  • J WolfeP., A Belabbas.
  • 2008

Matlab interface to svmlight

  • Anton Schwaighofer.
  • 2004

Similar Papers

Loading similar papers…