In this work, we incorporate new edges from a paraclique-identification approach to the output of the MST-kNN graph partitioning method. We present a statistical analysis of the results on a dataset originated from a computational linguistic study of 84 Indo-European languages. We also present results from a computational stylistic study of 168 plays of the Shakespearean era. For the latter, results of the Kruskal-Wallis test 1 (observed vs. all permutations) showed a p-value of a 1.62E-11 and a Wilcoxon test a p-value of 8.1E-12. Overall, our results clearly show in both cases that the modified approach provides statistically more significant results than the use of the MST-kNN alone, thus providing a highly-scalable alternative and statistically sound approach for data clustering.