The data can then be represented in a tree structure known as a dendrogram.

The dendrogram shown in the upper right plot of Figure 5also indicates four gene clusters.

In each step, similar subtrees (clusters) are merged to form the dendrogram.

Hence, the subsets can be represented using a tree diagram, or dendrogram.

The end result of the Girvan-Newman algorithm is a dendrogram.

The color code in the dendrogram depicts the origin of the fibroblast cultures.

The leaves of the dendrogram are individual nodes.

This method had previously produced a dendrogram that matched the expected sample biology in the pilot data set.

Similar clusters (subtrees) are successively merged to form a dendrogram.

A dendrogram is a broad term for the diagrammatic representation of a phylogenetic tree.

This is represented as a dendrogram.

Hierarchical algorithms define a dendrogram (tree) relating similar objects in the same subtrees.

The hierarchical clustering dendrogram would be as such:

Using a reachability-plot (a special kind of dendrogram), the hierarchical structure of the clusters can be obtained easily.

The sample dendrogram (Figure 2B) showed that the relatedness of the samples was similar to that observed with the pilot data set.

The relationships among genes listed in Table 2as determined through the analysis of literature content are represented by the dendrogram detailed in Figure 3.

Figure 10-6 Example of a cluster dendrogram, illustrating similarities and clustering of sites (x-axis) using biological data.

The Reviewed Dendrogram technique, relies on the experience of designers which may be limited to certain areas of expertise such as chemistry or electronics.

In general the major structure of the dendrogram remained the same, CC, Chr and normal kidney all grouped separately.

Each clustering produces a (non-unique) ordering, one that ensures that the branches of the corresponding dendrogram do not cross.

Agglomerative hierarchical clustering algorithms successively merge similar objects (or subtrees) to form a dendrogram.

The idea is to initialize the agglomerative algorithm by assigning repeated measurements of each object to the same subtrees in the dendrogram.

A common cluster dendrogram was produced by 18 of 40 methods; 16 of these were from the 20 that employed logarithmic transformation of the data sets.

A phylogenetic dendrogram derived from comparison of full-length OR protein sequences is shown in Figure 5, with the families bracketed.

To evaluate cluster quality, we obtain clusters from the dendrogram by stopping the merging process when the desired number of clusters (subtrees) is produced.