Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large number of measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the dictionary.
| Property | Value |
|---|
| dbpprop:abstract
|
- Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large number of measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the dictionary.
|
| dbpprop:date
| |
| dbpprop:harvtxtProperty
|
- Kriegel
- Kröger
- Zimek
- 2009 (xsd:integer)
|
| dbpprop:relatedInstance
| |
| dbpprop:wikiPageUsesTemplate
| |
| rdfs:comment
|
- Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large number of measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the dictionary.
|
| rdfs:label
|
- Clustering high-dimensional data
|
| skos:subject
| |
| foaf:page
| |
| is dbpprop:redirect
of | |
![[RDF Data]](/statics/sw-cube.png)
