# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 3 Experiment
### Deep Mining of End-User Electricity Consumption Information Big Data
Cluster three reaches its peak from 19:00 to 21:00, while cluster four peaks during 9:00 to 11:00 and again in the afternoon from 15:00 to 17:00, aligning well with typical user electricity consumption patterns. In Figure 8e, cluster five shows a relatively uniform daily load curve with no distinct features, mainly including users such as plastic factories, glass processing plants, and telecommunication companies. This clustering result demonstrates the effectiveness of the spectral clustering algorithm in obtaining typical user electricity consumption behaviors, clustering users based on their spatiotemporal distribution characteristics, and deeply mining data information in various user categories in the electricity market. To effectively evaluate the experimental results, common evaluation metrics including accuracy (ACC), normalized mutual information (NMI), and adjusted Rand index (ARI) were utilized. The proposed algorithm in this paper was compared with other algorithms using these metrics. The other algorithms include the following. SC: The main idea of spectral clustering is to treat samples as points in space, with lower weights between points that are distant and higher weights between points that are close. It involves cutting the graph formed by all samples to maximize edge weights within clusters [36]. LSC-K: Large-scale spectral clustering based on label representation [37]. CKM: Large-scale clustering using K-means algorithm [38]. LSR: Subspace clustering algorithm utilizing least squares regression [39]. The experimental results of each algorithm are listed in Table 1, and Figure 9 shows the computational time required for the corresponding algorithm on the data set. Among them, the SC algorithm has the worst experimental results because its main idea is to regard data as points in space, which is poorly adapted to the power data in the experiment. At the same time, its calculation is relatively complex and time-consuming. The CKM algorithm reduces computation time for large-scale clustering by using K-means, achieving good results in NMI but needing improvement in ACC and ARI.The LSR algorithm achieves good results across all metrics but takes a longer computation time. The proposed algorithm improves ACC and ARI by 3.69% and 2.6%, respectively, compared to the next best LSR algorithm, and NMI by 3.14% compared to CKM, demonstrating the best clustering performance. Moreover, the best result was achieved in calculation time, which  
Figure 7: The results of symbolic re-expression of user curves. (**a**) The result of segment aggregation approximate, (**b**) The result of segmented aggregation approximation based on information entropy. was 8 s faster than the CKM algorithm. These experimental results confirm the feasibility and superiority of the proposed method, as well as its efficient computation speed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **ACC** & **NMI** & **ARI** \\ \hline SC & 65.32 & 63.34 & 55.32 \\ LSC-K & 74.31 & 71.28 & 60.05 \\ CKM & 73.54 & 75.10 & 71.78 \\ LSR & 80.82 & 74.23 & 73.53 \\ Ours & 84.51 & 78.24 & 76.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ACC, ARI, and NMI values of different algorithms. Figure 8: The results of class cluster. (**a**) Class cluster 1 result; (**b**) class cluster 2 result; (**c**) class cluster 3 result; (**d**) class cluster 4 result; (**e**) class cluster 5 result.