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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A highly efficient multi-core algorithm for clustering extremely large datasets
|
|---|---|
| Published in |
BMC Bioinformatics, April 2010
|
| DOI | 10.1186/1471-2105-11-169 |
| Pubmed ID | |
| Authors |
Johann M Kraus, Hans A Kestler |
| Abstract |
In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 5% |
| Chile | 1 | 1% |
| Sweden | 1 | 1% |
| Canada | 1 | 1% |
| Argentina | 1 | 1% |
| Korea, Republic of | 1 | 1% |
| Unknown | 73 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 28% |
| Researcher | 20 | 24% |
| Professor > Associate Professor | 8 | 10% |
| Student > Master | 8 | 10% |
| Student > Postgraduate | 4 | 5% |
| Other | 15 | 18% |
| Unknown | 4 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 33 | 40% |
| Agricultural and Biological Sciences | 27 | 33% |
| Biochemistry, Genetics and Molecular Biology | 5 | 6% |
| Mathematics | 3 | 4% |
| Arts and Humanities | 1 | 1% |
| Other | 6 | 7% |
| Unknown | 7 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 April 2013.
All research outputs
#18,335,133
of 22,705,019 outputs
Outputs from BMC Bioinformatics
#6,290
of 7,255 outputs
Outputs of similar age
#85,464
of 94,755 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 69 outputs
Altmetric has tracked 22,705,019 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 94,755 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.