Locally adaptive metrics for clustering high dimensional data
Locally adaptive metrics for clustering high dimensional data
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Publisher
New York: Springer Nature B.V
Journal title
Language
English
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Publisher
New York: Springer Nature B.V
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Contents
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information enc...
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Full title
Locally adaptive metrics for clustering high dimensional data
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TN_cdi_proquest_journals_230106788
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_230106788
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ISSN
1384-5810
E-ISSN
1573-756X
DOI
10.1007/s10618-006-0060-8
