# Weighted \(z\)-Distance-Based Clustering and Its Application to Time-Series Data
## 4 Experimental Results
### Non Time-Series Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & & **K-Means** & **DSKmeans** & **FCM** & **Gmm** & **DBSCAN** & **SCC-I** \\ \hline \multirow{4}{*}{Breast} & DBI & 1.2336 & 0.9218 & 1.2415 & 1.1938 & 1.0814 & 0.8836 \\  & DI & 0.0838 & **0.1457** & 0.0838 & 0.0853 & 0.1452 & 0.1452 \\  & SI & 0.5765 & **0.6613** & 0.5683 & 0.6028 & 0.5459 & 0.5459 \\ \hline \multirow{4}{*}{Heart} & DBI & 1.8844 & 1.9028 & 1.8983 & 1.8894 & **1.3821** & 1.4799 \\  & DI & 0.3644 & **0.3715** & 0.2499 & 0.2551 & 0.1787 & 0.3604 \\  & SI & **0.3484** & 0.3314 & 0.3444 & 0.3467 & 0.3458 & 0.2785 \\ \hline \multirow{4}{*}{Ionophere} & DBI & 0.5498 & 0.5813 & 1.7182 & 1.8497 & 2.0965 & **0.4926** \\  & DI & 0.4841 & 0.4480 & 0.0703 & 0.3837 & 0.1012 & **0.5706** \\  & SI & 0.5706 & 0.4944 & 0.4097 & 0.5162 & 0.5376 & **0.6050** \\ \hline \multirow{4}{*}{Musk} & DBI & 1.3069 & 1.3336 & 1.3594 & 1.2995 & 1.1038 & **0.7281** \\  & DI & 0.1894 & 0.2056 & 0.1963 & 0.2508 & 0.1559 & **0.5793** \\  & SI & **0.5321** & 0.5215 & 0.5182 & 0.5319 & 0.5278 & 0.5319 \\ \hline \multirow{4}{*}{Sonar} & DBI & 1.8199 & 1.7128 & 1.8483 & 1.6240 & 1.3181 & **0.5805** \\  & DI & 0.1337 & 0.2312 & 0.1786 & 0.2376 & 0.1458 & **0.3789** \\  & SI & 0.3319 & 0.3754 & 0.3318 & 0.3872 & 0.1541 & **0.4163** \\ \hline \multirow{4}{*}{SPECT} & DBI & 1.4525 & 1.2578 & 1.8714 & 1.2676 & 1.1655 & **0.3567** \\  & DI & 0.2585 & **0.3853** & 0.1566 & 0.3268 & 0.1379 & 0.1287 \\  & SI & 0.6739 & 0.7257 & 0.4610 & 0.7308 & 0.3021 & **0.7467** \\ \hline \multirow{4}{*}{Ecoli} & DBI & 1.1673 & 1.3571 & 1.8659 & 0.9335 & 0.8678 & **0.7825** \\  & DI & 0.0765 & **0.0956** & 0.0398 & 0.0661 & 0.0703 & 0.0947 \\  & SI & 0.4940 & 0.5685 & 0.2872 & 0.3981 & 0.5390 & **0.6269** \\ \hline \multirow{4}{*}{Glass} & DBI & 0.9956 & 1.1442 & 1.7902 & 1.1721 & 2.1930 & **0.6346** \\  & DI & 0.1489 & 0.0633 & 0.0442 & 0.1392 & 0.2051 & **0.2060** \\  & SI & **0.7042** & 0.4512 & 0.4421 & 0.5138 & 0.6672 & 0.6831 \\ \hline \multirow{4}{*}{Iris} & DBI & 0.8281 & **0.6113** & 0.8436 & 0.6172 & 0.8012 & 0.8157 \\  & DI & 0.0694 & **0.1448** & 0.0701 & 0.1056 & 0.0619 & 0.0899 \\  & SI & **0.6959** & 0.6656 & 0.6891 & 0.6461 & 0.6949 & **0.6959** \\ \hline \multirow{4}{*}{Yeast} & DBI & 1.3526 & 1.6716 & 2.7041 & 1.8856 & 1.0550 & **0.8318** \\  & DI & 0.0399 & 0.0327 & 0.0208 & 0.0321 & 0.0167 & **0.0400** \\  & SI & 0.3339 & 0.2959 & 0.0263 & 0.2911 & **0.8213** & 0.3321 \\ \hline \multirow{4}{*}{Breast\_Tis} & DBI & 0.8691 & 1.0705 & 1.0286 & 1.0715 & **0.7800** & 0.8583 \\  & DI & 0.1146 & 0.1096 & 0.0449 & 0.1041 & 0.1093 & **0.1174** \\  & SI & 0.5926 & 0.5330 & 0.4629 & 0.4960 & 0.5610 & **0.6109** \\ \hline \multirow{4}{*}{Seeds} & DBI & 0.9436 & 0.9612 & 0.9448 & **0.6905** & 0.9313 & 0.9451 \\  & DI & **0.1259** & 0.1125 & 0.0800 & 0.0988 & 0.0493 & **0.1259** \\  & SI & **0.6209** & 0.5972 & 0.6196 & 0.6196 & 0.6063 & 0.6208 \\ \hline \multirow{4}{*}{User\_Know} & DBI & **1.6013** & 1.6626 & 1.6933 & 1.6118 & 1.6979 & 1.7487 \\  & DI & 0.0957 & 0.0989 & 0.0842 & 0.0776 & 0.0758 & **0.1053** \\  & SI & **0.3097** & 0.2949 & 0.2473 & 0.2676 & 0.2593 & 0.3018 \\ \hline \multirow{4}{*}{Vehicle} & DBI & 1.1933 & 1.2974 & 1.6017 & 1.1883 & 1.0709 & **0.9569** \\  & DI & 0.0846 & 0.0884 & 0.0668 & 0.0951 & 0.0573 & **0.0969** \\  & SI & 0.4793 & 0.4372 & 0.3986 & 0.3872 & 0.4864 & **0.6170** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparisons of Davies–Bouldin index (DBI), Dunn index (DI), and Silhouette index (SI) on 14 non-time-series datasets.