See the Computational Methods section for further details on the residues choice

for each of the order TPGS pubmed ID:http://www.ncbi.nlm.nih.gov/pubmed/19667117 three data sets generated from the conformations of the MD trajectory. The SQD values as a function of the number of clusters for, UPGMA, WPGMA, Complete and Ward’s methods are showed in the graphs (a), (b), (c) and (d), respectively. The black points identify optimal partitioning solutions. doi:10.1371/journal.pone.0133172.g005 PLOS ONE | DOI:10.1371/journal.pone.0133172 July 28, 2015 17 / 25 An Approach for Clustering MD Trajectory Using Cavity-Based Features Table 3. Statistical evaluations for the optimal partitioning solutions obtained from the best partitions (lowest SQD value) of every clustering method. Third column indicates the number of medoids used in the statistical assessments. Average, standard deviation and variance were calculated for each set of medoids based on predicted FEB values. Last row indicates the statistical values for the MD’s full trajectory. Clustering Method k-means k-medoid UPGMA WPGMA Complete Ward’s Data Set Protein RMSD Protein RMSD Cavity Attribute Cavity Attribute Cavity Attribute Cavity Attribute MD trajectory k cluster 19 66 133 84 48 95 20,000 SQD 0.01 0.01 0.04 0.03 0.01 0.01 0.00 Average -6.61 -6.63 -6.58 -6.59 -6.59 -6.60 -6.58 Standard Deviation -0.70 -0.70 -0.72 -0.73 -0.69 -0.68 -0.72 Variance -0.54 -0.55 -0.56 -0.58 -0.51 -0.51 -0.57 doi:10.1371/journal.pone.0133172.t003 count, Table 3 shows statistical assessments from the PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19667219 optimal partitions (lowest SQD values) for every clustering method. Fig 4 shows the unbalanced SQD values generated by the partition-based clustering algorithms. Even though k-means and k-medoids methods are able to reach low SQD values, they present the statistical values, which were also calculated based on FEB values, far from those fo