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X Demographics
Mendeley readers
Attention Score in Context
| Title |
CLAG: an unsupervised non hierarchical clustering algorithm handling biological data
|
|---|---|
| Published in |
BMC Bioinformatics, August 2012
|
| DOI | 10.1186/1471-2105-13-194 |
| Pubmed ID | |
| Authors |
Linda Dib, Alessandra Carbone |
| Abstract |
Searching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known enough, like often is the case, supervised classification is not appropriate either. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| Germany | 1 | 2% |
| France | 1 | 2% |
| Switzerland | 1 | 2% |
| Spain | 1 | 2% |
| Australia | 1 | 2% |
| Unknown | 53 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 22 | 36% |
| Student > Ph. D. Student | 13 | 21% |
| Student > Bachelor | 5 | 8% |
| Student > Doctoral Student | 4 | 7% |
| Other | 4 | 7% |
| Other | 9 | 15% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 56% |
| Computer Science | 6 | 10% |
| Environmental Science | 3 | 5% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Mathematics | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 5 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 15 September 2012.
All research outputs
#14,148,857
of 22,673,450 outputs
Outputs from BMC Bioinformatics
#4,713
of 7,247 outputs
Outputs of similar age
#97,694
of 166,746 outputs
Outputs of similar age from BMC Bioinformatics
#51
of 104 outputs
Altmetric has tracked 22,673,450 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 30th percentile – i.e., 30% 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 166,746 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.