Sentence examples for clusters were classified from inspiring English sources

About 5% of the 'good' clusters were classified bad, but this probably overestimates the false positive rate of the classifier, since specific identification of 'good' clusters by rules proved difficult; visual inspection of the misclassified 'good' clusters indicates that >80% probably do not represent one single individual, i.e. they are not really 'good'good

After sorting, the clusters were classified into noise, single-unit or multi-unit based on: (i) the spike shape and its variance; (ii) the ratio between the spike peak value and the noise level; (iii) the interspike interval (ISI) distribution of each cluster; and (iv) the presence of a refractory period for the single units, i.e., less than 1% spikes within 3 ms ISI32.

Similarly to the visual analysis, the significant hypometabolic clusters were classified according to their location among the ten previously mentioned regions.

All other clusters were classified as "moving clusters".

When performing the cluster analysis with a fixed number of clusters some of the profiles could not be identified in each run – this occurred when two or more of the clusters were classified as the same profile based on definitions given in Table 1.

Clusters were classified by iteratively updating cluster centres.

Secondly, for the ith cluster of p1 and p2, n2 clusters are classified according to the probability density function of P N, with weight w ij of each cluster, as defined by (57) and (58).

All remaining clusters are classified as spines.

Different clusters are classified with varying degrees of accuracy.

The remaining 17 clusters are classified as "others" in Figure 3A.

This control was used to increase stringency and thus data points that fall outside clusters are classified as "no call".