# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 5 Experimental Evaluation  
### Datasets  
The University of California (UCR) Time-Series Classification Archive [20] is one of the most widely used and the largest labeled time-series data archives for classification,consisting of 128 datasets. Each dataset is divided into training and test data and is accompanied by performance indicators of several algorithms with different parameter settings. In this work, we use the univariate datasets of the UCR archive in order to support the claim that our Sparse Time-Series Clustering (STSC) framework leads to clusterings with respect to the \(\beta\)-DTW distance of similar (or even improved) quality (compared against applying \(k\)-means to the original datasets), but with significantly improved running time. Baseline results are available for most of the UCR datasets in [22]. Nevertheless, we chose to run the baseline \(k\)-means algorithm on all the datasets used for experimental evaluation. For the scope of this work, we focused on univariate datasets and omitted datasets for which, due to computational power considerations, we were not able to run the \(k\)-means algorithm with DTW metric (see Section 5.6 for more details).  
The datasets used in our experimental evaluation are synthetic, semi-synthetic, or real and originate from various domains. Each dataset is univariate and contains from 40 to 5000 time series. Although the time series within each dataset have the same length, the length varies across datasets, ranging from 60 to 1882. A concise summary of these datasets is provided in Table 1, including information such as the number of time series, the number of clusters, the length of each time series, and dataset type.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Size** & **Length** &
\begin{tabular}{c} **No. of** \\ **Classes** \\ \end{tabular} & **Type** \\ \hline
**Adiac** & 781 & 176 & 37 & IMAGE \\
**ArrowHead** & 211 & 251 & 3 & IMAGE \\
**Beef** & 60 & 470 & 5 & SPECTRO \\
**BeetleFly** & 40 & 512 & 2 & IMAGE \\ BirdChicken & 40 & 512 & 2 & IMAGE \\ Car & 120 & 577 & 4 & SENSOR \\ CBF & 930 & 128 & 3 & SIMULATED \\ Coffee & 56 & 286 & 2 & SPECTRO \\ Computers & 500 & 720 & 2 & DEVICE \\ CricketX & 780 & 300 & 12 & MOTION \\ CricketY & 780 & 300 & 12 & MOTION \\ CricketZ & 780 & 300 & 12 & MOTION \\
**DiatomSizeReduction** & 322 & 345 & 4 & IMAGE \\
**DistalPhalamxOutlineAgeGroup** & 539 & 80 & 3 & IMAGE \\
**DistalPhalamxCorrect** & 876 & 80 & 2 & IMAGE \\
**DistalPhalamxTW** & 539 & 80 & 6 & IMAGE \\ Earthquakes & 461 & 512 & 2 & SENSOR \\ ECG200 & 200 & 96 & 2 & ECG \\
**ECGFiveDays** & 884 & 136 & 2 & ECG \\
**FaceAll** & 2250 & 131 & 14 & IMAGE \\
**FaceFour** & 112 & 350 & 4 & IMAGE \\
**FaceUCR** & 2250 & 131 & 14 & IMAGE \\ FiftyWords & 905 & 270 & 50 & IMAGE \\ Fish & 350 & 463 & 7 & IMAGE \\
**GunPoint** & 200 & 150 & 2 & MOTION \\
**Ham** & 214 & 431 & 2 & SPECTRO \\
**Herring** & 128 & 512 & 2 & IMAGE \\
**InsectWingbeatSound** & 2200 & 256 & 11 & SENSOR \\
**ItalyPowerDenand** & 1096 & 24 & 2 & SENSOR \\
**LargeKitchenAppliances** & 750 & 720 & 3 & DEVICE \\
**Lightning2** & 121 & 637 & 2 & SENSOR \\
**Lightning7** & 143 & 319 & 7 & SENSOR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Description.