 Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious  alternative for identifying separable multivariate normal distributions, particularly those with  lower centroid separation  } .
<meaning-changed>  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious  alternative for identifying separable multivariate normal distributions, particularly those with  lower centroid separation  } .
 Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious alternative for identifying separable multivariate normal distributions, particularly those with lower centroid separation }=3). Overall, we recommend that researchers 1) only apply cluster analysis when large subgroup separation is expected, 2) aim for sample sizes of N=20 to N=30 per expected subgroup, 3) use multidimensional scaling to improve cluster separation, and 4) use fuzzy clustering or finite mixture modelling approaches that are more powerful and more parsimonious with partially overlapping multivariate normal distributions .