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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detection of Significant Groups in Hierarchical Clustering by Resampling
|
|---|---|
| Published in |
Frontiers in Genetics, August 2016
|
| DOI | 10.3389/fgene.2016.00144 |
| Pubmed ID | |
| Authors |
Paola Sebastiani, Thomas T. Perls |
| Abstract |
Hierarchical clustering is a simple and reproducible technique to rearrange data of multiple variables and sample units and visualize possible groups in the data. Despite the name, hierarchical clustering does not provide clusters automatically, and "tree-cutting" procedures are often used to identify subgroups in the data by cutting the dendrogram that represents the similarities among groups used in the agglomerative procedure. We introduce a resampling-based technique that can be used to identify cut-points of a dendrogram with a significance level based on a reference distribution for the heights of the branch points. The evaluation on synthetic data shows that the technique is robust in a variety of situations. An example with real biomarker data from the Long Life Family Study shows the usefulness of the method. |
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 % |
|---|---|---|
| Switzerland | 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 23% |
| Student > Master | 4 | 10% |
| Student > Ph. D. Student | 3 | 8% |
| Student > Doctoral Student | 3 | 8% |
| Professor | 3 | 8% |
| Other | 8 | 21% |
| Unknown | 9 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 21% |
| Medicine and Dentistry | 4 | 10% |
| Business, Management and Accounting | 3 | 8% |
| Computer Science | 3 | 8% |
| Social Sciences | 2 | 5% |
| Other | 8 | 21% |
| Unknown | 11 | 28% |
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 08 August 2016.
All research outputs
#20,337,210
of 22,882,389 outputs
Outputs from Frontiers in Genetics
#8,623
of 11,919 outputs
Outputs of similar age
#319,398
of 364,241 outputs
Outputs of similar age from Frontiers in Genetics
#52
of 52 outputs
Altmetric has tracked 22,882,389 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,919 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 1st percentile – i.e., 1% 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 364,241 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.