Sentence examples for clusters comparison from inspiring English sources

The unit of randomization was the CHW area (n = 133); 105 of these had participated in one of the three arms of the previous Projahnmo project (home-based care: n = 33; community-care: n = 32 clusters; comparison arm: n = 40 clusters).

Different clustering comparison or similarities functions have been proposed in the literature [2].

As mentioned earlier, clustering comparison has done using four performance quality metrics.

Below are the results of each cluster comparison with the two types of innovations.

The immediate visual perception that the resulting clusters are very similar in the two cases in Figures 2 top) and 2(down) is also confirmed by various cluster comparisons by Precision, Recall and F-measure, re-defined for clustering comparison (Figure 3).

Besides, the spatial arrangement of significant hotspots was mapped through kernel density estimation over two time scales (daily and seasonal), distinguishing among 41 sets, sequentially arranged to facilitate clustering comparison and determination of spatiotemporal risky areas.

The congruence between these multiple results can be quantified by clustering comparison measures such as the Wallace coefficient (W).

We compare discovered types to known types via cluster comparison metrics: cluster homogeneity, cluster completeness, and the ARI.

For PCA cluster comparison data from all the training sessions was pooled together to calculate common eigen vectors.

A cluster comparison was run between hierarchical clustering (correlation-based distance, average linkage) and k-means clustering (Euclidean distance, k = 10).

The clustering algorithm then begins its first iteration using the estimated k. Consecutive iterations consist of the cluster comparison and merge steps.