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We identified 18 putative species clusters with high support values and which we retrieved consistently.
A good clustering method will produce high quality clusters with high intra-class similarity and low inter-class similarity.
The TOF mass spectrometer is capable of analyzing nano-sized neutral metal oxide clusters with high mass resolution.
Cu Mg clusters with high Mg Cu ratio have the most strengthening potency during secondary ageing in T6I4 heat treatment.
K-means clustering algorithm takes input parameter k (number of clusters) and partition data into k clusters with high inter-cluster similarity based on distance function.
A good clustering algorithm must produce high-quality clusters with high levels of intra-cluster similarity and with low levels of inter-cluster similarity.
This can be explained by the presence of several clusters with high density of compounds in the dataset containing compounds of different classes.
Structural homogeneity within clusters, and structural differences between clusters with high RMSD differences of those individual regions, can be shown by hydrogen-bond analyses.
One attractive feature of our proposed algorithm is that it is able to find clusters with high precision rate (i.e., high purity).
These kinds of evaluation usually assign the best score to the algorithm that produces clusters with high similarity within a cluster and low similarity between clusters.
The algorithm considers node mobility during cluster formation, produces clusters with high stability and is also robust enough to channel error and exhibits reasonable overhead.
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