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Sketch and validate for big data clustering
Panagiotis A. Traganitis
, Konstantinos Slavakis
,
Georgios B Giannakis
Electrical and Computer Engineering
Digital Technology Center
Research output
:
Contribution to journal
›
Article
›
peer-review
23
Scopus citations
Overview
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Keyphrases
K-means
100%
Big Data Clustering
100%
Learning Tools
50%
Reduced Complexity
50%
High-dimensional Data
50%
Computer Vision
50%
Random Projection
50%
Competitive Performance
50%
Number of Dimensions
50%
Operation Mode
50%
Numerical Test
50%
Feature Vector
50%
Computational Efficiency
50%
Efficient Clustering
50%
Robust Regression
50%
Big Data Analytics
50%
Kernel Function
50%
Divergence Criterion
50%
Streaming Mode
50%
Separable Vector
50%
Reduction Algorithm
50%
Linearly Non-separable
50%
Computer Science
Big Data
100%
K-Means Clustering
100%
Clustered Data
100%
Feature Vector
100%
High Dimensional Data
50%
Random Projection
50%
Real Data Sets
50%
Reduction Algorithm
50%
Kernel Function
50%
Feature Variable
50%
Big Data Analytics
50%
Computational Efficiency
50%
Relative Performance
50%
Computer Vision
50%
Engineering
Dimensional Data
100%
Relative Performance
100%
Dimensionality
100%
Computational Efficiency
100%
Computervision
100%
Real Data
100%
Feature Vector
100%
Big Data
100%
Big Data Clustering
100%
Mathematics
K-Means
100%
Clustered Data
100%
Big Data
100%
Clustering
50%
Synthetic Data
50%
Robust Regression
50%
Modes of Operation
50%
Dimensional Data
50%
Data Analytics
50%
Real Data
50%
Reduction Algorithm
50%
Feature Vector
50%