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The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches and clustering operation to eliminate genetic drift.
Clustering operation, which attempts to partition a set of objects into several subsets that are distinguishable under some criterion of similarity, could significantly ease these operations.
By resorting to device fingerprints extracted from images, source-oriented image clustering can be divided into two main sequential operations: the extraction of device fingerprint from images followed by an image clustering operation based on the device fingerprints.
In EESSC, the sensor nodes' residual energy would be taken into account in clustering operation, and a special packet head is defined to help update nodes' energy information when transmitting message among the nodes.
Thus, with this appropriate set of global parameters, it is ensured that all the high dense points are separated from the less dense points [36, 37], and clustering operation is performed on high dense points only and LOFs are computed of less dense points.
Redundancy among protein sequences of each organism is removed by clustering operation using the blastclust executable program in the BLAST package [ 22] from NCBI with a threshold of 90% as sequence identity, and a nonredundant dataset is obtained by keeping only the longest sequence in each cluster for each organism [ 23].
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Most of these algorithms perform clustering operations.
Clustering operations were implemented at inflation values of 1.2.
The results of a hierarchical clustering procedure can be represented in a "dendrogram" plot, that conveys visually the sequence of agglomerative clustering operations and the linkage distance at each step.
All clustering operations were performed using the Genesis software [ 66].
These successive clustering operations produced a binary clustering tree (dendrogram), whose roots contained all the observations.
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