## 3 Results and Discussion
HDBSCAN and DBSCAN saw a decrease in performance, scoring 0.652 and 0.455, respectively, highlighting the complexity of clustering in noisy conditions. Figure 6 showcases the accuracy (ACC) performance for each experiment across the various clustering methods. Complementing this, Figure 7 provides a statistical overview, displaying the average ACC and standard deviation (SD) for each trial within Dataset 1. The table further breaks down the performance by difficulty level, offering a comparison of the average ACC and SD for two trials each in Easy 1 (E1), Easy 2 (E2), Difficult 1 (D1), and Difficult 2 (D2) within Dataset 2. This dual presentation of results in both graphic and tabular form allows for a comprehensive analysis of the clustering methods' performance. Examining further the specifics of clustering ACC performance, the G-NMS method stood out in the S1 category with an impressive ACC of 99.94% and a notably low SD of 0.01, highlighting its exceptional precision and reliability. K-means also performed well, achieving a 94.40% ACC, but with a higher SD of 9.64, its results were less consistent. Agglomerative clustering and FCM exhibited moderate accuracy levels, scoring 88.11% and 91.65%, respectively, yet their higher SDs suggest greater variability in their results. HDBSCAN and DBSCAN presented lower ACCs and greater inconsistencies, particularly HDBSCAN, with a significantly lower ACC of 75.66% and a high SD of 34.67. ISO-SPLIP trailed with the least effectiveness, attaining only a 47.52% ACC and a high SD of 26.92. In the E1 category, the G-NMS method maintained its superior performance, securing an ACC of 99.68% and an SD of 0.15, reaffirming its efficiency and consistency. K-means showed a closely competitive ACC of 98.19% and an SD of 1.95, while agglomerative clustering achieved an ACC of 98.77% with an SD of 0.64. FCM was slightly less accurate at 98.17%. HDBSCAN and DBSCAN demonstrated significant progress from their S1 performance, with both surpassing the 90% ACC mark. ISO-SPLIP, however, continued to underperform, with an ACC of 74.85% and a high SD of 35.51. Figure 6: Algorithmic accuracy in cluster analysis across diverse experimental conditions. The bar chart demonstrates comparable accuracy of various clustering algorithms, including K-means, G-NMS*, agglomerative, HDBSCAN, DBSCAN, FCM, and ISO-SPLIT, as applied to different experiments with specific SNR values and neural spike class (Spk) combinations. The E2 category saw the G-NMS method lead narrowly with a 99.27% ACC and a minimal SD of 0.15. K-means and agglomerative clustering also exhibited strong performances, with ACCs of 98.65% and 97.94%, respectively. FCM posted an ACC close to that of K-means at 98.47%. HDBSCAN and DBSCAN, while improved, did not reach the top-tier accuracies. ISO-SPLIP remained the least effective, with a 73.31% ACC and a high SD of 37.56. In the D1 category, there was a notable shift in performance dynamics. The G-NMS method saw a decrease in ACC to 74.23% and an increase in SD to 7.91. K-means led this dataset with an 87.33% ACC and an SD of 12.93. FCM also showed strong performance with an 88.51% ACC and an SD of 13.81. Agglomerative clustering and DBSCAN recorded moderate accuracies of 83.03% and 70.84%, respectively. However, HDBSCAN and ISO-SPLIP struggled with both ACC and consistency, especially HDBSCAN with an ACC of 44.14% and a high SD of 62.42. Finally, in the D2 category, FCM outperformed others, attaining the highest ACC of 99.50% and an impressively low SD of 0.07. The G-NMS method, while still performing well, did not match FCM's ACC, posting a 95.38% ACC and an SD of 0.74. K-means and agglomerative clustering both exhibited high ACCs of 98.56% and 98.27%, respectively, with low SDs. HDBSCAN and DBSCAN, on the other hand, had lower ACCs and higher variability, with HDBSCAN achieving an ACC of 86.43% and DBSCAN 73.86%.