Title |
QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization
|
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Published in |
PLOS ONE, January 2011
|
DOI | 10.1371/journal.pone.0014468 |
Pubmed ID | |
Authors |
Mario Inostroza-Ponta, Regina Berretta, Pablo Moscato |
Abstract |
The visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain "hidden regularities" and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 4% |
Portugal | 1 | 4% |
Australia | 1 | 4% |
Unknown | 23 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 31% |
Researcher | 7 | 27% |
Student > Master | 5 | 19% |
Other | 2 | 8% |
Student > Doctoral Student | 1 | 4% |
Other | 2 | 8% |
Unknown | 1 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 35% |
Agricultural and Biological Sciences | 6 | 23% |
Social Sciences | 3 | 12% |
Engineering | 3 | 12% |
Mathematics | 2 | 8% |
Other | 3 | 12% |