Automatic Subspace Clustering of High Dimensional Data
Automatic Subspace Clustering of High Dimensional Data
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Publisher
New York: Springer Nature B.V
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Language
English
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Publisher
New York: Springer Nature B.V
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Contents
Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a cluster...
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Full title
Automatic Subspace Clustering of High Dimensional Data
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TN_cdi_proquest_journals_230156224
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_230156224
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ISSN
1384-5810
E-ISSN
1573-756X
DOI
10.1007/s10618-005-1396-1
