The likelihood was maximized across possible cluster locations, sizes, and shapes to identify the most likely disease cluster.
In order to obtain stable carbon clusters, all amorphous structures were taken with most possible symmetry as initial configurations.
Thus, parametrically exploring all possible cluster sizes was possible.
This would put most possible attackers off.
Figure 5 shows the gene expression profiles for the most significant clusters, 6 out of 100 possible clusters.
The tests performed on decoy sets are representative of a possible clustering scenario in the context of most representative model selection.
A most simple clustering algorithm is an exhaustive search of all possible clustering patterns (exhaustive algorithm) [20].
One common problem with most of these clustering algorithms is to find the optimal number of possible clusters for the data set.
The advantage of this approach is that it can distribute credentials in a pseudo-random fashion within the network and avoid possible "clustering," where a small group of nodes end up storing most of the credentials.
