# Clustering on the Chicago Array of Things: Spotting Anomalies in the Internet of Things Records
## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Notable Clusters** & **Cluster Count** &
\begin{tabular}{c} **Heuristic** \\ **Anomalies** \\ \end{tabular} \\ \hline
001e0610f02f & & & \\ \hline bmp180 & 2: 319,133, 4: 302,412, \(-1\): 8 & 6 & 302,412 \\ htu21d & 4: 312,895, 16: 302,364, \(-1\): 8 & 201 & 302,364 \\ pr103j2 & 13: 741,737, \(-1\): 98,564 & 17 & 91 \\ tmp112 & 0: 514,774, 2: 325,022, \(-1\): 10 & 4 & 325,022 \\ tsys01 & not clustered & & \\ \hline
001e06113d20 & & & \\ \hline bmp180 & 4: 623,832, 3: 364,467, 6: 146,584, \(-1\): 7 & 8 & 146,584 \\ htu21d & 16: 361,405, 32: 146,585, \(-1\): 16 & 788 & 146,585 \\ pr103j2 & 194: 8314, 191: 8271, \(-1\): 1 & 408 & 0 \\ tmp112 & 7: 152,812, 9: 10,178, \(-1\): 5 & 633 & 185,959 \\ tsys01 & not clustered & & \\ \hline
001e0610 ba3b & & & \\ \hline bmp180 & 1: 140,282, 0: 444 & 2 & 0 \\ htu21d & \(-1\): 139,592 & 1 & 57 \\ pr103j2 & 57: 3306, 73: 3296, \(-1\): 3 & 522 & 0 \\ tmp112 & \(-1\): 140,951 & 1 & 14 \\ tsys01 & not clustered & & \\ \hline
001e06113f54 & & & \\ \hline bmp180 & 1: 503,497, 2: 373,091, \(-1\): 6 & 6 & 30,275 \\ htu21d & 25: 212,746, 26: 199,184, 32: 30,270, \(-1\): 21 & 287 & 30,270 \\ pr103j2 & 484: 7897, 482: 7645, 230: 59, \(-1\): 18 & 557 & 59 \\ tmp112 & 7: 41,991, 9: 13,826, \(-1\): 3 & 622 & 89,119 \\ tsys01 & not clustered & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: H-DBSCAN cluster counts vs. heuristically determined anomalies for each sensor. The DB Scores are better (the clusters are more valid) the closer to 0 the value gets. The K-Means algorithm has the best scores of all three algorithms, with all scores below 1 and most scores below 0.1. This would indicate the clusters chosen were dense and separable. The DB Scores for DBSCAN were generally much higher, with all but 1 score above 0.1 and 9 of 15 reported scores above 1. Some cases (node f02\(\acute{t}\), sensor pr103\(\acute{2}\), node 3f54, and sensor htu21d) had very high DB score values. This indicates the clusters were not that separable. This is likely due to the lack of dimensionality in the dataset. With this metric, H-DBSCAN fared better than DBSCAN, having two scores below 0.1 and only \begin{table}
\begin{tabular}{c c c} \hline
**Node** & **Sensor** & **DB Score** \\ \hline
001e0610f02\(\acute{t}\) & bmp180 & 0.038824672921208406 \\
001e0610f02\(\acute{t}\) & htu21d & 0.07690979909983497 \\
001e0610f02\(\acute{t}\) & pr1032 & na \\
001e0610f02\(\acute{t}\) & tmp112 & 0.02882118033585473 \\
001e0610f02\(\acute{t}\) & tsys01 & 0.04384870955257482 \\ \hline
001e06113d20 & bmp180 & 0.036814840183613945 \\
001e06113d20 & htu21d & 0.0998322274257292 \\
001e06113d20 & pr103j2 & 0.4865105225567226 \\
001e06113d20 & tmp112 & 0.029372036351903098 \\
001e06113d20 & tsys01 & 0.042269436817583314 \\ \hline
001e0610 ba3b & bmp180 & na \\
001e0610 ba3b & htu21d & na \\
001e0610 ba3b & pr103j2 & 0.5345483627490976 \\
001e0610 ba3b & tmp112 & na \\
001e06113e3b & tsys01 & na \\ \hline
001e0611354 & bmp180 & 0.041739643131029645 \\
001e0611354 & htu21d & 0.8394572503561166 \\
001e0611354 & pr103j2 & 0.428926620784116 \\
001e0611354 & tmp112 & 0.03241690084035104 \\
001e0611354 & tsys01 & 0.04727127733130374 \\ \hline \end{tabular}
\end{table} 
Table 6: The Davies–Bouldin scores for each node–sensor combination, K-Means algorithm.