Mining Hierarchies of Correlation Clusters

@article{Achtert2006MiningHO,
  title={Mining Hierarchies of Correlation Clusters},
  author={Elke Achtert and C. B{\"o}hm and P. Kr{\"o}ger and A. Zimek},
  journal={18th International Conference on Scientific and Statistical Database Management (SSDBM'06)},
  year={2006},
  pages={119-128}
}
  • Elke Achtert, C. Böhm, +1 author A. Zimek
  • Published 2006
  • Computer Science
  • 18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
    • The detection of correlations between different features in high dimensional data sets is a very important data mining task. These correlations can be arbitrarily complex: one or more features might be correlated with several other features, and both noise features as well as the actual dependencies may be different for different clusters. Therefore, each cluster contains points that are located on a common hyperplane of arbitrary dimensionality in the data space and thus generates a separate… CONTINUE READING
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    46 Citations
    Background Citations
    7
    Methods Citations
    14
    Results Citations
    1
    46 Citations
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    References

    SHOWING 1-10 OF 21 REFERENCES
    Computing Clusters of Correlation Connected objects
    • 176
    • PDF
    Density-Connected Subspace Clustering for High-Dimensional Data
    • 401
    • PDF
    CURLER: finding and visualizing nonlinear correlation clusters
    • 88
    • PDF
    OP-cluster: clustering by tendency in high dimensional space
    • J. Liu, W. Wang
    • Mathematics, Computer Science
    • Third IEEE International Conference on Data Mining
    • 2003
    • 204
    • PDF
    Fast algorithms for projected clustering
    • 1,106
    • PDF
    Finding generalized projected clusters in high dimensional spaces
    • 517
    • PDF
    Finding generalized projected clusters in high dimensional spaces
    • 226
    • PDF
    Clustering by pattern similarity in large data sets
    • 547
    • PDF
    OPTICS: ordering points to identify the clustering structure
    • 3,274
    • PDF
    Automatic subspace clustering of high dimensional data for data mining applications
    • 2,593
    • PDF