In pattern recognition, the iDistance is an indexing and query processing technique for k-nearest neighbor queries on point data in multi-dimensional metric spaces. The kNN query is one of the hardest problems on multi-dimensional data, especially when the dimensionality of the data is high. The iDistance is designed to process kNN queries in high-dimensional spaces efficiently and it is especially good for skewed data distributions, which usually occur in real-life data sets.
| Property | Value |
|---|
| dbpedia-owl:thumbnail
| |
| dbpprop:abstract
|
- In pattern recognition, the iDistance is an indexing and query processing technique for k-nearest neighbor queries on point data in multi-dimensional metric spaces. The kNN query is one of the hardest problems on multi-dimensional data, especially when the dimensionality of the data is high. The iDistance is designed to process kNN queries in high-dimensional spaces efficiently and it is especially good for skewed data distributions, which usually occur in real-life data sets.
|
| dbpprop:reference
| |
| rdfs:comment
|
- In pattern recognition, the iDistance is an indexing and query processing technique for k-nearest neighbor queries on point data in multi-dimensional metric spaces. The kNN query is one of the hardest problems on multi-dimensional data, especially when the dimensionality of the data is high. The iDistance is designed to process kNN queries in high-dimensional spaces efficiently and it is especially good for skewed data distributions, which usually occur in real-life data sets.
|
| rdfs:label
| |
| skos:subject
| |
| foaf:depiction
| |
| foaf:page
| |
| is dbpprop:redirect
of | |
![[RDF Data]](/statics/sw-cube.png)
