# EliNAC: Autoencoder Approach for Electronic Invoices Data Clustering
## 4 Performance Evaluation
### Discussion  
The performed experiments presented valuable results, which did not diverge much from the initial expectations. Analyzing the results, there was a significant distinction between ELINAC and DBSCAN, which are more time-efficient, and K-means and Hierarchical, which are more time-consuming. While ELINAC and DBSCAN had similarities in clustering metrics, they also performed better when considering the clustering time. For K-means and Hierarchical, clustering metrics showed significantly better results with the penalty of taking more extended, especially for the Hierarchical algorithm.  
The Hierarchical algorithm, which used the agglomerative approach, had a significant disadvantage in performance, unlike ELINAC. However, it was very consistent for the clustering metrics, especially for more generalist clusters. While this Hierarchical approach  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Time** \\ \hline ELINAC & 206.6 s/epoch \\ K-means & - \\ DBSCAN & 10,210.979 s \\ Hierarchical & - \\ \hline \end{tabular}
\end{table}
Table 6: Clustering times for the 1 million NF-es dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Clusters** & **True Negatives** & **True Positives** & **False Negatives** & **False Positives** \\ \hline ELINAC & Specific & 445,145,242 & 455,518 & 163,810 & 99,770 \\ K-means & Specific & 445,162,158 & 494,676 & 124,652 & 82,854 \\ DBSCAN & Specific & 444,983,742 & 521,610 & 97,718 & 261,270 \\ Hierarchical & Specific & 445,058,290 & 519,462 & 99,866 & 186,722 \\ ELINAC & Generalist & 443,694,596 & 509,258 & 1,293,434 & 367,052 \\ K-means & Generalist & 443,919,118 & 676,700 & 1,125,992 & 142,530 \\ DBSCAN & Generalist & 403,291,136 & 1,671,470 & 131,222 & 40,770,512 \\ Hierarchical & Generalist & 443,935,750 & 1,412,886 & 389,806 & 125,898 \\ \hline \end{tabular}
\end{table}
Table 7: Evaluation of each method’s performance when assigning records to specific and generalist clusters.  
may not be viable for clustering extensive NF-e data, it presented clear advantages in searching for clusters with more variation.  
K-means was one of the most consistent approaches, being similar to Hierarchical in most metrics and maintaining close results for generalist clusters. While it performed significantly faster than Hierarchical, its time complexity showed to be a barrier for more extensive datasets, as opposed to ELINAC.  
DBSCAN is a common choice for time-efficient clustering solutions due to its complexity. The algorithm presented itself as a balanced solution, providing good clustering results while not consuming much time. The main disadvantage for the method was the inconsistency observed in the Homogeneity and Fowlkes-Mallows metrics, especially when forming homogeneous clusters for the generalist label, unlike ELINAC.  
ELINAC accomplished what was proposed: clustering data fast and efficiently. The clustering time results showed that not only is the approach time-effective; however, it also managed to outperform the second fastest algorithm (i.e., DBSCAN) by almost three times regarding the clustering time. Therefore, ELINAC was able to cluster similar to other well-known approaches while doing so considerably faster, demonstrating its advancement to the state-of-the-art.