# Two Medoid-Based Algorithms for Clustering Sets
## 5 Clustering Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **bSDM** & **bARI** & **bCI** & **SR** & **bSI** & **aARI** & **aCI** \\ \hline data\_1200\_100\_16\_5\_1 & 1513 & 0.99 & 0 & 6.0\% & 0.71 & 0.72 & 2.71 \\ \hline data\_1200\_200\_4\_5\_1 & 7986 & 1.0 & 0 & 85.5\% & 0.89 & 0.94 & 0.15 \\ \hline data\_1200\_200\_8\_5\_1 & 4827 & 0.99 & 0 & 41.5\% & 0.79 & 0.86 & 0.74 \\ \hline data\_1200\_200\_16\_0\_1 & 1419 & 1.0 & 0 & 2.2\% & 0.83 & 0.83 & 2.21 \\ \hline data\_1200\_200\_16\_5\_1 & 3124 & 0.99 & 0 & 7.5\% & 0.68 & 0.72 & 2.68 \\ \hline data\_1200\_200\_16\_5\_2 & 3320 & 0.99 & 0 & 5.1\% & 0.69 & 0.73 & 2.91 \\ \hline data\_1200\_200\_16\_5\_3 & 3434 & 0.99 & 0 & 1.8\% & 0.70 & 0.77 & 3.24 \\ \hline data\_1200\_200\_16\_5\_4 & 4110 & 0.98 & 0 & 0.6\% & 0.72 & 0.77 & 3.91 \\ \hline data\_1200\_200\_16\_5\_5 & 5111 & 0.99 & 0 & 0.4\% & 0.72 & 0.79 & 3.62 \\ \hline data\_1200\_200\_16\_10\_1 & 3262 & 0.99 & 0 & 4.1\% & 0.67 & 0.65 & 3.14 \\ \hline data\_1200\_200\_16\_20\_1 & 3472 & 0.99 & 0 & 1.9\% & 0.64 & 0.56 & 3.54 \\ \hline data\_1200\_200\_16\_40\_1 & 4628 & 0.89 & 1 & 0.0\% & 0.50 & 0.41 & 3.86 \\ \hline data\_1200\_200\_32\_5\_1 & 2136 & 0.94 & 1 & 0.0\% & 0.55 & 0.54 & 7.32 \\ \hline data\_1200\_400\_16\_5\_1 & 6016 & 1.0 & 0 & 8.8\% & 0.68 & 0.70 & 2.72 \\ \hline data\_1200\_800\_16\_5\_1 & 10,285 & 0.99 & 0 & 21.8\% & 0.66 & 0.79 & 1.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of repeated K-medoids (1000 runs) with uniform random seeding on the datasets of Table 1 with weighted Jaccard distance. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **bSDM** & **bARI** & **bCI** & **SR** & **bSI** & **aARI** & **aCI** \\ \hline data\_1200\_100\_16\_5\_1 & 1513 & 0.99 & 0 & 39.6\% & 0.71 & 0.93 & 0.69 \\ \hline data\_1200\_200\_4\_5\_1 & 7986 & 1.0 & 0 & 99.1\% & 0.89 & 1.0 & 0.01 \\ \hline data\_1200\_200\_8\_5\_1 & 4827 & 0.99 & 0 & 78.0\% & 0.79  
From the results in Tables 4 and 5, it emerges that for _almost_ all the datasets, 1000 repetitions of K-medoids with uniform random seeding are sufficient for obtaining a good clustering solution. However, the use of G-K-means++ seeding not only enables a very good solution to be achieved in all the cases, as reflected by the values of the \(ARI\), \(C1\), and \(SI\) indexes, but it also increases significantly the _Success Rate_. Only for the two datasets \(data\_1200\_200\_16\_40\_1\) and \(data\_1200\_200\_32\_5\_1\), 1000 repetitions of K-medoids with uniform random seeding proved to be insufficient for generating \begin{table}
\begin{tabular}{l l l l l l} \hline
**Dataset** & **SDM** & **ARI** & **CI** & **SI** & **Iterations** \\ \hline data\_1200\_100\_16\_5\_1 & 1513 & 0.99 & 0 & 0.71 & 26 \\ \hline data\_1200\_200\_4\_5\_1 & 7986 & 1.0 & 0 & 0.89 & 5 \\ \hline data\_1200\_200\_8\_5\_1 & 4825 & 0.99 & 0 & 0.79 & 51 \\ \hline data\_1200\_200\_16\_0\_1 & 1419 & 1.0 & 0 & 0.83 & 2 \\ \hline data\_1200\_200\_16\_5\_1 & 3124 & 0.99 & 0 & 0.68 & 10 \\ \hline data\_1200\_200\_16\_5\_2 & 3320 & 0.99 & 0 & 0.69 & 16 \\ \hline data\_1200\_200\_16\_5\_3 & 3434 & 0.99 & 0 & 0.70 & 42 \\ \hline data\_1200\_200\_16\_5\_4 & 4202 & 0.98 & 0 & 0.72 & 27 \\ \hline data\_1200\_200\_16\_5\_5 & 5000 & 0.99 & 0 & 0.72 & 99 \\ \hline data\_1200\_200\_16\_10\_1 & 3262 & 0.99 & 0 & 0.67 & 60 \\ \hline data\_1200\_200\_16\_20\_1 & 3480 & 0.99 & 0 & 0.64 & 14 \\ \hline data\_1200\_200\_16\_40\_1 & 3467 & 0.99 & 0 & 0.59 & 63 \\ \hline data\_1200\_200\_32\_5\_1 & 1825 & 0.99 & 0 & 0.59 & 85 \\ \hline data\_1200\_400\_16\_5\_1 & 6016 & 1.0 & 0 & 0.68 & 13 \\ \hline data\_1200\_800\_16\_5\_1 & 10287 & 0.99 & 0 & 0.66 & 9 \\ \hline \end{tabular}
\end{table} 
Table 6: Results of parallel random swap (max 100 iterations) with uniform random seeding, on the datasets of Table 1 with weighted Jaccard distance.