# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 5 Experimental Evaluation
### Results for the Mocap and Energy Datasets
#### 5.5.1 Accuracy
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score Energy (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-9}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.58** & 0.35 & **0.47** & 0.29 & **0.44** & 0.27 & **0.42** & 0.27 \\
75\% & **0.47** & 0.21 & **0.37** & 0.17 & **0.35** & 0.16 & **0.34** & 0.16 \\
90\% & **0.43** & 0.12 & **0.35** & 0.1 & **0.32** & 0.08 & **0.31** & 0.08 \\
100\% & **0.37** & 0.07 & **0.30** & 0.05 & **0.28** & 0.05 & **0.26** & 0.05 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score Energy (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-9}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.57** & 0.48 & **0.52** & 0.42 & **0.47** & 0.38 & **0.44** & 0.35 \\
75\% & **0.61** & 0.43 & **0.56** & 0.31 & **0.51** & 0.27 & **0.47** & 0.25 \\
90\% & **0.63** & 0.34 & **0.58** & 0.25 & **0.52** & 0.23 & **0.49** & 0.21 \\
100\% & **0.56** & 0.23 & **0.51** & 0.20 & **0.47** & 0.19 & **0.43** & 0.18 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score Energy (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-9}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.58** & 0.35 & **0.47** & 0.29 & **0.44** & 0.27 & **0.42** & 0.27 \\
75\% & **0.47** & 0.21 & **0.37** & 0.17 & **0.35** & 0.16 & **0.34** & 0.16 \\
90\% & **0.43** & 0.12 & **0.35** & 0.1 & **0.32** & 0.08 & **0.31** & 0.08 \\
100\% & **0.37** & 0.07 & **0.30** & 0.05 & **0.28** & 0.05 & **0.26** & 0.05 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score MoreCap (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-9}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.57** & 0.48 & **0.52** & 0.42 & **0.47** & 0.38 & **0.44** & 0.35 \\
75\% & **0.61** & 0.43 & **0.56** & 0.31 & **0.51** & 0.27 & **0.47** & 0.25 \\
90\% & **0.63** & 0.34 & **0.58** & 0.25 & **0.52** & 0.23 & **0.49** & 0.21 \\
100\% & **0.56** & 0.23 & **0.51** & 0.20 & **0.47** & 0.19 & **0.43** & 0.18 \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score Energy (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**}  
**Varying Motif Instance Counts and Length Flexibilities. ** In Table 5, we present the results under differing motif instance counts and varying length flexibilities. Comparing the results reported in this table with the default results in Table 4, we can seen that both algorithms are similarly robust against repeating motif instance counts; on the other hand, the results indicate that SMM-s is significantly more accurate than MPC when subsequence lengths are allowed to be flexible (\(f=50\)-\(100\%\)). **Varying the Degree of Variate Alignment. ** In this set of experiments, we relax one other constraint; _motif instances can be discovered across different variates (e.g., left and right arms)_.