Sentence examples for cluster evaluation from inspiring English sources

Cluster evaluation is usually carried out using graphical methods.

They are (i) use of agglomerative hierarchical clustering algorithms and (ii) use of the silhouette coefficient for cluster evaluation.

One drawback of using internal criteria in cluster evaluation is the high scores on an internal measure do not truly result in data clustering.

To calculate F1, specialists may include subjective criteria for deciding on similarity or dissimilarity of search-returned images: criteria missing on the characteristics described by the images signatures that directly affect cluster evaluation through silhouette coefficient calculation.

The DNA sample set included 6 replicate pairs and 94 trios to aid in cluster evaluation.

Likewise, all cluster evaluation parameters showed a significantly better performance of the best-scoring cluster versus the remaining clusters.

Furthermore, findings of the independent cluster evaluations were also used to corroborate the observations of the authors.

Algorithms can then be evaluated using classical extrinsic clustering evaluation metrics [52].

When a ground-truth community structure is available, classical external clustering evaluation indices can be used to evaluate and compare community detection algorithms.

To evaluate the optimal number of clusters, we calculate the clustering evaluation object containing Davies-Bouldin index values and find the minimum criterion value.

To evaluate the optimal number of clusters, we create a clustering evaluation object containing Davies-Bouldin index values in Figure 3(E).