# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 4 Experiment Results and Analysis
### Experimental Results and Comparison Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Taxi GPS Dataset** & **Algorithms** & **Maximum** & **Average** & **Minimum** \\ \hline \multirow{4}{*}{Aracaju (Brazil)} & Noise K-means & 0.03201 & 0.03108 & 0.02999 \\  & FCM & 0.01319 & 0.01286 & 0.01259 \\  & K-means & 0.03049 & 0.02827 & 0.02681 \\  & K-means plus & 0.03229 & 0.0308 & 0.02912 \\  & Noise K-means & 0.01218 & 0.01172 & 0.01136 \\  & FCM & 0.01034 & 0.00958 & 0.00886 \\  & K-means & 0.01132 & 0.01041 & 0.00985 \\  & K-means plus & 0.01278 & 0.01193 & 0.01114 \\  & Noise K-means & 0.0236 & 0.022548 & 0.022006 \\  & FCM & 0.011832 & 0.010484 & 0.009661 \\  & K-means & 0.020837 & 0.019411 & 0.017145 \\  & K-means plus & 0.023151 & 0.022623 & 0.021651 \\  & Noise K-means & 0.0627 & 0.05794 & 0.05468 \\  & FCM & 0.03976 & 0.03731 & 0.03568 \\  & K-means & 0.06013 & 0.0542 & 0.05118 \\  & K-means plus & 0.06133 & 0.05753 & 0.0532 \\  & Noise K-means & 0.06086 & 0.0598 & 0.05833 \\  & FCM & 0.03976 & 0.03731 & 0.03568 \\  & K-means & 0.06157 & 0.05877 & 0.05694 \\  & K-means plus & 0.06011 & 0.06036 & 0.05914 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of PBM index evaluation values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Taxi GPS Dataset** & **Algorithms** & **Maximum** & **Average** & **Minimum** \\ \hline \multirow{4}{*}{Aracaju (Brazil)} & Noise K-means & 0.08366 & 0.07992 & 0.07423 \\  & FCM & 0.17978 & 0.15106 & 0.13756 \\  & K-means & 0.08817 & 0.07815 & 0.06961 \\  & K-means plus & 0.0858 & 0.78368 & 0.06975 \\  & Noise K-means & 0.09342 & 0.09039 & 0.08518 \\  & FCM & 0.15793 & 0.1459 & 0.12684 \\  & K-means & 0.10198 & 0.08553 & 0.08055 \\  & K-means plus & 0.09755 & 0.09082 & 0.08518 \\  & Noise K-means & 0.16462 & 0.10853 & 0.09498 \\  & FCM & 0.17961 & 0.15236 & 0.13591 \\  & K-means & 0.09967 & 0.09159 & 0.08619 \\  & K-means plus & 0.16606 & 0.10897 & 0.09622 \\  & Noise K-means & 0.11173 & 0.10038 & 0.09182 \\  & FCM & 0.17093 & 0.14996 & 0.13769 \\  & K-means & 0.10028 & 0.09 & 0.0847 \\  & K-means plus & 0.11207 & 0.10867 & 0.0922 \\  & Noise K-means & 0.10447 & 0.09777 & 0.09215 \\  & FCM & 0.17093 & 0.14996 & 0.13769 \\  & K-means & 0.0925 & 0.08668 & 0.08281 \\  & K-means plus & 0.10459 & 0.09799 & 0.09305 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of DB index evaluation values. The SSE convergence curves for the FCM, K-means, K-means plus, and noise-based K-means methods are shown in Figures 4-8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Taxi GPS Dataset** & **Algorithms** & **Maximum** & **Average** & **Minimum** \\ \hline \multirow{6}{*}{Aracaju (Brazil)} & Noise K-means & 19.3463 & 18.1769 & 17.4351 \\  & FCM & 28.7974 & 28.0926 & 27.3564 \\  & K-means & 21.4902 & 20.4493 & 18.193 \\ \cline{2-5}  & K-means plus & 19.957 & 19.0552 & 17.4351 \\ \cline{2-5}  & Noise K-means & 34.8698 & 33.8601 & 33.1321 \\ \cline{2-5}  & FCM & 41.1925 & 40.0136 & 38.6125 \\ \cline{2-5}  & K-means & 39.1769 & 37.2797 & 35.5356 \\ \cline{2-5}  & K-means plus & 35.6768 & 34.6172 & 34.0928 \\ \cline{2-5}  & Noise K-means & 54.1587 & 52.2404 & 50.6664 \\ \cline{2-5}  & FCM & 86.4097 & 82.9769 & 77.1727 \\ \cline{2-5}  & K-means & 64.186 & 59.7377 & 56.1926 \\ \cline{2-5}  & K-means plus & 54.7747 & 52.7017 & 51.1269 \\ \cline{2-5}  & Noise K-means & 102.901 & 99.4066 & 95.5405 \\ \cline{2-5}  & FCM & 151.343 & 144.9 & 135.923 \\ \cline{2-5}  & K-means & 109.436 & 104.691 & 101.017 \\ \cline{2-5}  & K-means plus & 108.112 & 101.987 & 99.5079 \\ \cline{2-5}  & Noise K-means & 151.343 & 144.9 & 135.923 \\ \cline{2-5}  & FCM & 224.152 & 214.794 & 209.467 \\ \cline{2-5}  & K-means & 223.885 & 220.258 & 216.065 \\ \cline{2-5}  & K-means plus & 220.629 & 218.108 & 216.092 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of SSE evaluation values.