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Hence, the full distribution of the number of clusters estimated by each method is presented in Table 3. Note that only the methods Clest, gap, gapPC and hart have the capability to identify one cluster in the data.
Neither gap nor gapPC were able to identify the presence of the two clusters for Model 6, which is a model with two drawn-out clusters and seven noise variables with varying variances.
Note that of the above methods, only hart, gap, and gapPC allow the estimation of only one cluster in the data, that is, = 1.
Both gap and gapPC consistently estimated one cluster for this model, perhaps because both methods are based on the within-clusters sums of squares and consequently are more affected by the variables with larger variances.
For each number of clusters k, the plots of the median values, over the 50 simulated datasets, of the Clest d k -statistic, gapPC k , and