Dimension Reduction for Model-based Clustering via Mixtures of ...

Figure 4.3: Pairs plot for the three overlapping clusters in Model 3 (n = 300 data points).• scenario three (redundant and noise variables): started with scenario one andadded three variables correlated with each clustering variable (with correlationcoefficients equal to 0.9, 0.7, 0.5, respectively) as well as four independent standardnormal variables.In order to ascertain the performance of the clustering methods under varying datadimensions, each scenario was run for three data sets consisting of 100, 300 and 1000data points generated according to the schemes described earlier. Every run comprised1000 simulations. Table 4.1 shows the comparison between our results for tMMDR andthe results for GMMDR from Scrucca (2010).For the scenarios which include noise and redundant variables, tMMDR exhibits ARIvalues which are higher than those for GMMDR. This occurs consistently for all modelsand varying data dimensions.26