Sentence examples for silhouette method from inspiring English sources

The cluster analysis using Silhouette method resulted in the number of clusters K = 140, which were the clusters at A-level (Fig. 6).

To find an optimal threshold on the number of clusters, we use the Silhouette method, which compares the tightness and separation of clusters [42].

Since the k-means method requires as an input the number of clusters, we used the silhouette method [46], [47] to estimate the number of clusters that fitted best.

The silhouette method is based on the production of a silhouette profile for each cluster found, which defines how good is the classification of each seed in comparison with its assignment to a second most appropriate cluster.

In order to determine the most appropriate number of clusters in this specific context, we used a variant of the silhouette method [ 28].

An effective silhouette representation method computes discriminative and compact silhouette descriptors which are used for learning the silhouette-pose mapping, and a good silhouette matching algorithm enables effective comparison and search in the example database.

This section begins with a review of different ways to represent a 3D object and the reasons for our choice of a multi-view silhouette-based method.

There are other methods that can be used to find optimal number of clusters in the data, e.g. the Silhouettes criterion method Rousseeuw (1987), Davies-Bouldin's criterion method Davies and Bouldin (1979) and Calinski-Harabasz criterion method Caliński and Harabasz (1974).

For these methods, silhouette representation and matching is very important.

The within-method silhouette and homogeneity metrics allowed us to look "under the hood" at individual clusters and make inferences on them.

Notations used in Table 7 are described as, ({ hbox{min} }_{D}^{p}) is minimum or close to minimum value of DBI and ({ hbox{max} }_{S}^{p}) is maximum or close to maximum value of Silhouette coefficient with proposed method; (N_{o}^{p}) is optimal or close to optimal number of clusters with proposed method.