Sentence examples for meta clusters from inspiring English sources

The meta k-means algorithm then identifies cells that are co-clustered in more than 80% of k-means ++ runs and groups them into meta clusters initially.

These meta clusters are then iteratively merged, based on the similarity of their averaged, activity traces, to yield a variable, unspecified number of final clusters, of un-specified sizes.

It resulted in five meta-clusters, shown in the lower section of stripes in Figure 1.

Even then, however, some of the meta-clusters were not adequately discriminated by their acoustical properties.

MDA is the Mean Decrease Accuracy in classification of the five meta-clusters by the acoustic features using RF.

The classification results imply that the acoustic correlates of the clusters can be established if we are looking only at the broadest semantic level (meta-clusters).

Figure 3 Normalized distribution of the three most important features for classification of the five meta-clusters by means of RF analysis.

For 19 clusters, this resulted in 950 excerpts per set; and for the 5 meta-clusters, it resulted in 250 excerpts per set.

For the meta-clusters however, the task was more feasible and the classification accuracy was significantly higher: 54.8% for the prediction per test set (with a chance level of 20%).

Interestingly, the meta-clusters were found to differ quite widely in their classification accuracy: Energetic (I, 34%), Intimate (II, 66%), Classical (III, 52%), Mellow (IV, 50%) and Cheerful (V, 72%).

The constituent systems comprise innovation meta-networks (networks of innovation networks and knowledge clusters) and knowledge meta-clusters (clusters of innovation networks and knowledge clusters) organized in a self-referential or chaotic fractal (Gleick1987) knowledge and innovation architecture (Carayannis2001).