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Beyond the fence was a rough cluster of unfinished concrete buildings.
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We find that a rough, cluster-dominated structure evolves into a terrace-step morphology, with increasing temperature.
As the parameter k must be estimated from cluster sizes, a rough clustering iteration is performed using a large number of links (k defaults to 6).
We suggest that users of k -link perform a rough clustering (without parameter estimation, option -E) to identify any significantly large clusters.
The GC-MS data set for fully-mature berries was also investigated by PCA, and showed a rough clustering based on vintage.
To validate whether use of a rough clustering (k = 6) was a good enough approximation to the 'true' distribution of clusters, we estimated the probability bounds for co-occurrence using k = 2, 3.
The purpose of this rough clustering is just to obtain a skimmed video and also to remove redundant or unimportant frames for reducing computations for the following keyframe selection steps.
We organize the ideas into rough clusters so that information about the same topic is close together.
The rough clustering is then discarded.
Despite these differences, each rough clustering suggested the same number of required links (k = 3, at the 0.01 significance level) as that estimated from the reference clustering for the dataset.
Lingras and West [20] introduced a rough set-inspired clustering algorithm based on the well-known c-means algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com