Finding generalized projected clusters in high dimensional spaces

@inproceedings{Aggarwal2000FindingGP,
  title={Finding generalized projected clusters in high dimensional spaces},
  author={C. Aggarwal and Philip S. Yu},
  booktitle={SIGMOD '00},
  year={2000}
}
  • C. Aggarwal, Philip S. Yu
  • Published in SIGMOD '00 2000
  • Computer Science
  • High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimensional data, even the concept of proximity or clustering may not be meaningful. We discuss very general techniques for projected clustering which are able to construct clusters in arbitrarily aligned subspaces of lower dimensionality. The subspaces are specific to the clusters themselves. This definition is substantially… CONTINUE READING
    517 Citations

    Figures, Tables, and Topics from this paper

    Subspace selection for clustering high-dimensional data
    • 66
    • PDF
    Density connected clustering with local subspace preferences
    • 172
    • PDF
    Finding interesting subspace clusters from high dimensional datasets
    • 1
    Projective clustering by histograms
    • 67
    • PDF
    On discovery of extremely low-dimensional clusters using semi-supervised projected clustering
    • K. Yip, D. Cheung, M. Ng
    • Computer Science
    • 21st International Conference on Data Engineering (ICDE'05)
    • 2005
    • 75
    • PDF
    Subspace clustering for high dimensional data: a review
    • 1,254
    • Highly Influenced
    • PDF
    Projective clustering of high dimensional data
    • Highly Influenced
    Subspace clustering for high dimensional categorical data
    • 77
    • Highly Influenced
    • PDF
    Mining Subspace Clusters in High Dimensional Data
    • 3
    • PDF

    References

    SHOWING 1-2 OF 2 REFERENCES
    BIRCH: an efficient data clustering method for very large databases
    • 4,598
    • Highly Influential
    • PDF
    Algorithms for Clustering Data
    • 10,234
    • Highly Influential