A New Cluster Validity for Data Clustering
A New Cluster Validity for Data Clustering
About this item
Full title
Author / Creator
Yang, Xulei , Cao, Aize and Song, Qing
Publisher
Dordrecht: Springer
Journal title
Language
English
Formats
Publication information
Publisher
Dordrecht: Springer
Subjects
More information
Scope and Contents
Contents
Cluster validity has been widely used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity, which is called the Vapnik–Chervonenkis-bound (VB) index, for data clustering. It is estimated based on the structural risk minimization (SRM) principle, which optimizes the bound simultaneously over bot...
Alternative Titles
Full title
A New Cluster Validity for Data Clustering
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2918338215
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2918338215
Other Identifiers
ISSN
1370-4621
E-ISSN
1573-773X
DOI
10.1007/s11063-006-9005-x
