Sentence examples for clusters we use from inspiring English sources

Since the competing algorithms cannot detect the number of clusters, we use the value from the ground truth.

For the evaluation of the consistency of constructed clusters, we use global clinical dementia rating score which is interpreted as clinically normal CN (value 0), mild cognitive impairment MCI (value 0.5) and Alzheimer's disease AD (value 1) diagnosis for the patient.

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

To validate clusters, we use the Silhouette width [ 12] to measure their validity.

To generate QD clusters, we use a technique invented by Pease [ 26].

To distribute the generated objects in naturally looking clusters, we use the weighted distribution method described in the Population Distribution Generation section with randomly generated weight-maps.

To choose the number of clusters, we used the average silhouette index [39].

To create the clusters we used both name and email; we first normalized the names, then we looked for name similarities, name-email similarities, and email similarities.

To visualize the connectivity among clusters, we used Visone [58].

Additionally for analyzing enrichment in SOM clusters, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) [6], [55].

Focussing on these two major clusters, we used DAVID software (Table S2) to conduct a gene ontogeny analysis [20], [21].