# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 4 Results
### MOO Comparisons  
We compare CM-BBPSO with other MOC algorithms from our previous work. We present the results on three datasets, CMC, Segment and Vehicle, in Tables 2-4 which were not published in [4] due to run issues for clustering algorithms using multiobjective evolutionary algorithms with the global loop update (MG) [33], indicated as \(NA\). The results for the other 10 datasets were published. In these three challenging datasets, CM-BBPSO performed competitively, particularly for CMC where it had the second best accuracy, F1, Kappa and BSS. In this comparison, CM-BBPSO did not outperform any other MOC, nor BBPSO techniques. Instead, M-Fuzzy emerged as the superior algorithm in these datasets for most evaluation metrics. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Clus Algo** & **Accuracy** & **F1** & **Kappa** & **BSS** & **WSS** & **QE** \\ \hline CM-BBPSO & \(0.374\pm 0.02\) & \(0.34\pm 0.032\) & \(0.168\pm 0.0263\) & \(11\),\(100\pm 2310\) & \(2950\pm 194\) & \(3.66\pm 0.59\) \\ IB-Fuzzy & \(0.369\pm 0.03\) & \(0.351\pm 0.03\) & \(0.160\pm 0.04\) & \(8450\pm 2430\) & \(2810\pm 283\) & \(3.33\pm 0.60\) \\ IB-QPSO & \(0.375\pm 0.03\) & \(0.321\pm 0.04\) & \(0.171\pm 0.03\) & \(8440\pm 2730\) & \(2940\pm 144\) & \(4.00\pm 0.83\) \\ M-Fuzzy & \(\mathbf{0.387\pm 0.03}\) & \(0.355\pm 0.04\) & \(\mathbf{0.185\pm 0.03}\) & \(10\),\(200\pm 1360\) & \(\mathbf{2610\pm 86}\) & \(\mathbf{3.16\pm 0.29}\) \\ M-QPSO & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) \\ MG-BBPSO & \(0.378\pm 0.02\) & \(0.328\pm 0.03\) & \(0.175\pm 0.03\) & \(10\),\(600\pm 2200\) & \(2780\pm 125\) & \(3.79\pm 0.83\) \\ MG-Fuzzy & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) \\ MG-QPSO & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) \\ MG-BBPSO & \(0.369\pm 0.02\) & \(0.323\pm 0.04\) & \(0.162\pm 0.03\) & \(9870\pm 1820\) & \(2770\pm 134\) & \(3.68\pm 0.85\) \\ SP-QPSO & \(0.378\pm 0.02\) & \(0.296\pm 0.03\) & \(0.179\pm 0.02\) & \(8210\pm 2010\) & \(2740\pm 114\) & \(4.42\pm 1.04\) \\ BBPSO & \(0.379\pm 0.02\) & \(\mathbf{0.360\pm 0.03}\) & \(0.173\pm 0.03\) & \(\mathbf{17}\),\(500\pm 3130\) & \(3110\pm 308\) & \(3.69\pm 0.36\) \\ \hline \end{tabular}
\end{table} 
Table 4: Performance of CM-BBPSO and other clustering algorithms on Vehicle dataset with the best result produced by the algorithm presented in bold. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Clus Algo** & **Accuracy** & **F1** & **Kappa** & **BSS** & **WSS** & **QE** \\ \hline CM-BBPSO & \(0.402\pm 0.02\) & \(0.371\pm 0.04\) & \(0.089\pm 0.05\) & \(6590\pm 1540\) & \(4150\pm 186\) & \(2.88\pm 0.25\) \\ IB-Fuzzy & \(0.393\pm 0.02\) & \(0.368\pm 0.04\) & \(0.081\pm 0.05\) & \(5650\pm 1380\) & \(4260\pm 194\) & \(2.9\pm 0.14\) \\ IB-QPSO & \(0.401\pm 0.02\) & \(0.321\pm 0.05\) & \(0.056\pm 0.05\) & \(3980\pm 1130\) & \(4120\pm 157\) & \(2.97\pm 0.29\) \\ M-Fuzzy & \(\mathbf{0.404\pm 0.02}\) & \(\mathbf{0.383\pm 0.03}\) & \(\mathbf{0.100\pm 0.04}\) & \(5310\pm 1280\) & \(\mathbf{3950\pm 174}\) & \(\mathbf{2.72\pm 0.15}\) \\ M-QPSO & \(0.402\pm 0.01\) & \(0.356\pm 0.04\) & \(0.069\pm 0.04\) & \(5820\pm 1370\) & \(4050\pm 129\) & \(2.86\pm 0.23\) \\ M-BBPSO & \(0.400\pm 0.02\) & \(0.352\pm 0.03\) & \(0.063\pm 0.04\) & \(5230\pm 973\) & \(4020\pm 142\) & \(2.77\pm 0.19\) \\ MG-Fuzzy & \(0.389\pm 0.02\) & \(0.365\pm 0.04\) & \(0.084\pm 0.05\) & \(6020\pm 1660\) & \(4250\pm 225\) & \(2.93\pm 0.18\) \\ MG-QPSO & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) \\ MG-BBPSO & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) \\ MG-BBPSO & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) & \(NA\) \\ SP-QPSO & \(0.403\pm 0.02\) & \(0.331\pm 0.04\) & \(0.056\pm 0.04\) & \(3740\pm 988\) & \(3970\pm 142\) & \(2.75\pm 0.21\) \\ BBPSO & \(0.390\pm 0.02\) & \(0.355\pm 0.05\) & \(0.064\pm 0.06\) & \(\mathbf{16}\),\(\mathbf{6}\),\(\mathbf{6}\)\(\mathbf{0}\)\(\mathbf{0}\)\(\mathbf{3}\)\(\mathbf{6}\)\(\mathbf{8}\) & \(5080\pm 504\) & \(3.49\pm 0.33\) \\ \hline \end{tabular}
\end{table}