Sentence examples for most similar clusters from inspiring English sources

Step 2 (Identification of the two most similar clusters): the similarity matrix is scanned and the highest value simbest is identified, with corresponding pair of clusters denoted as (c i, c j ).

Table showing the average number of query compounds (out of 60) that have a similarity > = 0.7 to the most similar clusters The drawback to this technique is that the identifications of the point of interactions are limited to compounds which are similar to those used in the initial clustering analysis.

It then proceeds by merging the most similar clusters to produce a sequence of clusters.

The two most similar clusters are combined as per reduction stage complying the given 'k' value for a number of clusters (k < n).

Starting with the pairwise correlation matrix, the algorithms initially consider each SPN as a cluster and iteratively merge the two most similar clusters according to the average linkage criterion.

The algorithm successively merges the two most similar clusters based on the distance function until there is one single cluster.

Then, each remaining transcript is compared to the representatives of all existing clusters and would be clustered to the most similar cluster if the similarity is above threshold of global sequence identity of ≥ 90%.

The best predictions are made if the cluster centre or the average similarity to a cluster is used to calculate the most similar cluster.

The cluster which had the most similar compound; 2. The cluster which had the most similar cluster centre; 3. The cluster with the highest average similarity; 4. Repeat considering only clusters with a minimum of 1 (i.e. singletons), 2, 3, 4 or 5 compounds.

If the predefined similarity threshold is met, the sequence is grouped into the most similar cluster.

The figure shows the predictions when the similarity to the cluster centre is used to determine the most similar cluster.