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Robust, fuzzy, and parsimonious clustering based on mixtures of Factor Analyzers

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Academic year: 2020

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Figure 1 shows, in the left panel, a specimen of randomly generated data from the given mixture, while in the right panel it reports results obtained
Fig. 1 Left panel: synthetic data (data drawn from X 1 in green, from X 2 in red, and contamination drawn from X 3 and X 4 in black)
Fig. 2 Left panel: Fuzzy classification of the synthetic data, with c = 10 and G = 2. Right panel: Almost unconstrained fuzzy classification of the synthetic data, with c = 10 10 and G = 3, generating a spurious solution
Fig. 3 Effects of the value of the fuzzifier parameter m on the cluster membership values (for panels (a), (b) and (c) we set m = 1, m = 1.1 and m = 1.13; while for panels (d), (e) and (f) we set m = 1.16, m = 1.17 and m = 1.2, respectively)
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