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As kurtosis and skewness effectively describe the shape of distributions, they are good parameter choices for clustering distributions into profiles according to shape properties.
While short time series datasets such as presented here are becoming more common, there are still few choices for clustering that are tailored towards this type of data.
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In the Rosenberg study, the choice for a panel with indigenous populations is already a choice for clustered populations.
Although there are many choices of clustering approaches, hierarchical clustering [ 21] is the most commonly used one for microarray analysis.
A standard error, p value and 95%% confidence intervals (CI) were calculated for each coefficient based on all choices within each group, without adjustment for clustering of preferences at the respondent level.
Furthermore, the choice of using the AP algorithm for clustering the similarity matrices has the advantage over classical clustering methods in the fact that AP can determine the appropriate number k of clusters, depending on a vector of the median of similarities as input preference for all genes in the mouse genome.
Since the transport layer protocol naturally distinguishes between TCP and UDP flows, it was a natural choice for a feature to be used for clustering.
The choice of the number of clusters (k) remains challenging for clustering methods.
The three steps are: variable/feature selection, choice of similarity measure and choice of clustering technique.
Notably, subcutaneous sumatriptan and oxygen inhalation, the treatments of first choice for cluster headache attacks, were not effective.
Algorithms for clustering data.
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