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GPU accelerated self-join for the distance similarity metric
Michael Gowanlock
, Ben Karsin
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
5
Scopus citations
Overview
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Dive into the research topics of 'GPU accelerated self-join for the distance similarity metric'. Together they form a unique fingerprint.
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Computer Science
Distance Calculation
100%
And-States
50%
Data Mining
50%
Euclidean Distance
50%
Efficient Algorithm
50%
Parallel Algorithms
50%
Dimensionality Problem
50%
Performance Bottleneck
50%
Building-Blocks
50%
Dimensionality Increase
50%
Dimensional Data
50%
Distance Metric
50%
Big Data Application
50%
Keyphrases
GPU Acceleration
100%
Self-join
100%
Similarity Metrics
100%
Distance Similarity
100%
Low Dimensionality
42%
Distance Calculation
28%
Parallel Algorithm
14%
Low Quality Data
14%
Data Density
14%
Performance Bottleneck
14%
Set Size
14%
Bounded Search
14%
Dimensionality Problem
14%
Euclidean Distance Measure
14%
Big Data Applications
14%
Increasing Dimension
14%
Curse of Dimensionality
14%
Engineering
Metrics
100%
Similarities
100%
Graphics Processing Unit
100%
Dimensionality
71%
Euclidean Distance
14%
Building Block
14%
Big Data
14%
Dimensional Data
14%