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The main objective of clustering is to subdivide data objects into smaller groups known as clusters so that each group exhibits a high degree of intra-cluster similarity and inter-cluster dissimilarity [5].
In addition to providing increased statistical power to detect subtle effects, it becomes possible to subdivide data and retain statistical power to test for differential effects.
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Inside each sphere or square are more spheres or squares, subdividing data into finer categories.
It subdivides data acquisition into a set of hierarchical tasks, bonding data structure and the operations to be performed tightly together.
Subdividing data according to the strongest geo-climatic gradient in each dataset aimed to reduce the strength of natural descriptors relative to land use.
We performed this comparison using the subdivided data set presented in Fig. 5 (A).
In the previous section, we used the subdivided data sets obtained from the data set in Fig. 2 (A).
These highly subdivided data contained many zeros and are not presented in this paper although the authors have them available for later use.
All statistical tests were carried out in R [29], and made use of the smatr package [23], including its common slope test ("slope.com"), which compares the fit of a one- and two-slope model to the subdivided data.
The subdivided data sets were also processed by using the modified version [7] of the iterative helical real space refinement (IHRSR) method [28] resulting in another two reconstructions per condition.
The NRY sample sizes were too small to allow analysis of subdivided data.
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