Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
About this item
Full title
Author / Creator
Publisher
Boston: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
Boston: Springer US
Subjects
More information
Scope and Contents
Contents
Stability of a learning algorithm with respect to small input perturbations is an important property, as it implies that the derived models are robust with respect to the presence of noisy features and/or data sample fluctuations. The qualitative nature of the stability property enhardens the development of practical, stability optimizing, data min...
Alternative Titles
Full title
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_1730064888
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_1730064888
Other Identifiers
ISSN
1384-5810
E-ISSN
1573-756X
DOI
10.1007/s10618-013-0320-3
