Evaluating Clustering in Subspace Projections of High Dimensional Data

  title={Evaluating Clustering in Subspace Projections of High Dimensional Data},
  author={Emmanuel M{\"u}ller and Stephan G{\"u}nnemann and Ira Assent and Thomas Seidl},
Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation and comparison between these paradigms on a common basis. Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the “true” clusters in real… CONTINUE READING
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Fast algorithms for projected clustering

  • C. Aggarwal, J. Wolf, P. Yu, C. Procopiuc, J. Park
  • SIGMOD, pages 61–72,
  • 1999
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