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The non-parametric surrogate data (or bootstrap) method, is described here in its basic form, valid for independent and identically distributed data, but is readily extendible to the treatment of dependent data.
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The bootstrap methods are described in more detail below.
Each method is described, as follows.
The results from three different approaches: the Average linkage method, the scree plot of within groups sum of squares, ratio of between-cluster variability and within-cluster variability, and the Multi-scale bootstrap of Ward's method are described and discussed below in (a), (b) and (c).
The suitability of the bootstrap method is evaluated in two ways, which are described in the following.
(The poll methods are described here).
Methods are described in [30].
(Methods are described in Text S4).
Both methods are described below.
Both methods are described in this section.
The conceptual block diagram of the radius selection algorithm using bootstrap method is shown in Figure 2 and the detailed steps are described below.
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