Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators

@article{Ferdosi2010FindingAV,
  title={Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators},
  author={Bilkis J. Ferdosi and Hugo Buddelmeijer and Scott C. Trager and Michael H. F. Wilkinson and Jos B. T. M. Roerdink},
  journal={2010 IEEE Symposium on Visual Analytics Science and Technology},
  year={2010},
  pages={35-42}
}
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. However, many clustering algorithms require the user to set a large number of… CONTINUE READING
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