# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 4 Conclusions  
To solve the problem of low clustering accuracy and difficulty in expanding to large datasets, we proposed to convert time-series datasets into two-dimensional images and designed a deep clustering network to improve the effect of unsupervised clustering. Through comparative analysis, the GADF images showed the best characterization effect among the four image representation methods. This method could map the low-dimensional data to the high-dimensional space for revealing the time correlation of the data. Therefore, the main contributions in this study were the integration of the GADF transformation method and wavelet transform as well as the design of the DC network for clustering according to enhanced two-dimensional images. The wavelet transform method, applied in the image enhancement stage, could enhance the texture features of two-dimensional images and enlarge the relevant attributes of the data (Figure 10). Then, in the clustering stage, the DC  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **SAE +K-Means**[31] & **AE +K-Means**[32] & **AE\_Conv +K-Means** \\ \hline
**Index** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** \\ \hline BME & 22.37 & 55.56 & 20.62 & 50.69 & 16.27 & 55.56 \\ CBF & 27.69 & 51.08 & 23.24 & 48.39 & 26.89 & 58.47 \\ SmoothSubspce & 6.21 & 50.00 & 8.13 & 50.83 & 19.64 & 75.00 \\ SonyAIBORobotSurface2 & 24.29 & 71.43 & 0.19 & 67.22 & 13.34 & 64.29 \\ SyntheticControl & 35.71 & 47.50 & 15.68 & 28.12 & 15.78 & 39.58 \\ UMD & 26.78 & 52.78 & 14.30 & 46.53 & 18.63 & 55.56 \\ \hline
**Dataset** & \multicolumn{2}{c}{**DEC**[22]} & \multicolumn{2}{c}{**DEC\_FCN**} & \multicolumn{2}{c}{**GW\_DC**} \\ \hline
**Index** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** \\ \hline BME & 46.69 & 58.33 & 44.54 & 52.78 & 91.18 & 61.13 \\ CBF & 30.32 & 76.88 & 32.77 & 63.44 & 54.77 & 80.65 \\ SmoothSubspce & 28.01 & 53.33 & 23.37 & 51.67 & 42.43 & 65.00 \\ SonyAIBORobotSurface2 & 30.17 & 79.08 & 14.30 & 76.02 & 25.74 & 77.04 \\ SyntheticControl & 24.72 & 53.33 & 52.82 & 40.00 & 71.45 & 66.67 \\ UMD & 33.74 & 63.89 & 16.46 & 66.67 & 46.53 & 75.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different deep clustering models.  
network based on CNN was designed and used to learn the characteristics of images, and the K-Means algorithm was applied for unsupervised clustering. The convolution kernel factorization method was introduced to reduce the amount of parameter calculation and improve clustering performance. The CE was cited as the loss function to optimize the objective function. Through experimental analysis, the proposed GW-DC model showed a better clustering effect than other models; the comparison result is described in Section 3.3, and the maximum difference in NMI reached 44.49%.  
Conceptualization, T.L., X.L. and Y.W.; data curation, Y.W.; formal analysis, T.L.; funding acquisition, T.L.; investigation, Y.W.; methodology, X.L. and Y.W.; project administration, T.L.; resources, Y.W.; supervision, T.L.; validation, X.L. and Y.W.; visualization, Y.W.; writing-original draft, T.L. and Y.W.; writing-review and editing, T.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China, grant number 51939001, and 61976033, the National Social Science Foundation of China, grant number 15 CGL031, Young Foundation of Ministry of Education Humanities and Social Sciences, grant number 21 YJC630066, the Liaoning Revitalization Talents Program, grant number XLYC1907084, the Science & Technology Innovation Funds of Dalian, grant number 2018J11 CY022, the Fundamental Research Funds for the Central Universities, grant number 3132019353 and 3132021273.  
Not applicable.  
The authors declare no conflict of interest.