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As a result, automated multivariate clustering has become highly desirable for objective and reproducible assessment of high dimensional FC data.
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Multivariate clustering method has been employed to divide the samples into a number of groups for the purpose of both representing the reservoir heterogeneity and simplifying rock typing for dynamic modeling.
Because of its utility, clustering has emerged as one of the leading methods of multivariate analysis [3].
The cluster has disassembled in certain ascomycetes.
A final cohesive slice is considered as a normal multivariate cluster if all components have a normal distribution.
Similarity relationships were investigated by means of multivariate clustering, similarity percentage analysis and nm-MDS ordination; the two experimental designs have been tested by permutational MANOVA and analysis of dispersion (PERMDISP).
Large clusters have a smaller probability of following a multivariate distribution than small ones.
The result multivariate clustering is expected to be roughly elliptical [13].
Multivariate clustering of expression patterns identified 5 clusters of co-expressed compounds with common chemical characteristics.
Using multivariate clustering techniques we found 5 clusters of compounds as shown in Figure 4.
More ancient clusters have been identified.
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