# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 4 Results and Discussion
### PCP Configuration Experiments
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Simple+LE** & **Weighted+LE** & **Simple+UMAP** & **Weighted+UMAP** \\ \hline \multirow{4}{*}{K-Means} & Dor & 0.5634 (C2) & 0.7437 (C1) & 0.5820 (C2) & 0.5201 (C1) \\ \cline{2-5}  & Maj & 0.6610 (C3) & 0.5366 (C3) & 0.5520 (C1) & 0.5031 (C2) \\ \cline{2-5}  & Min & 0.6737 (C1) & 0.7017 (C1) & 0.9225 (C4) & 0.7469 (C3) \\ \cline{2-5}  & Mix & 0.7767 (C2) & 0.7869 (C1) & 0.8220 (C2) & 0.6238 (C1) \\ \hline \multirow{4}{*}{Agglom.} & Dor & 0.5282 (C3) & 0.7083 (C3) & 0.7329 (C3) & 0.5244 (C2) \\ \cline{2-5}  & Maj & 0.7726 (C1) & 0.6174 (C3) & 0.5506 (C2) & 0.6771 (C1) \\ \cline{2-5}  & Min & 0.6668 (C3) & 0.6701 (C3) & 0.9717 (C3) & 0.8077 (C2) \\ \cline{2-5}  & Mix & 0.5847 (C4) & 0.7800 (C3) & 0.7178 (C1) & 0.6240 (C4) \\ \hline \multirow{4}{*}{DBSCAN} & Dor & 1.0 (C1) & 1.0 (C1) & 0.9963 (C1) & 1.0 (C1) \\ \cline{2-5}  & Maj & 1.0 (C1) & 1.0 (C1) & 0.9908 (C2) & 1.0 (C1) \\ \cline{2-5}  & Min & 1.0 (C1) & 1.0 (C1) & 0.9989 (C1) & 1.0 (C1) \\ \cline{2-5}  & Mix & 1.0 (C1) & 1.0 (C1) & 0.8270 (C1) & 1.0 (C1) \\ \hline \multirow{4}{*}{Mean Shift} & Dor & 0.9953 (C1) & 0.9685 (C1) & 0.9936 (C1) & 0.9711 (C1) \\ \cline{2-5}  & Maj & 0.9767 (C1) & 0.9484 (C1) & 0.9508 (C2) & 0.9966 (C1) \\ \cline{2-5}  & Min & 0.9460 (C1) & 0.9617 (C1) & 0.9989 (C1) & 0.9784 (C1) \\ \cline{2-5}  & Mix & 0.9937 (C1) & 0.9764 (C1) & 0.8270 (C1) & 0.5347 (C1) \\ \hline \multirow{4}{*}{SOM} & Dor & 1.0 (C3) & 1.0 (C2) & **0.9766 (C2)** & 1.0 (C1) \\ \cline{2-5}  & Maj & 1.0 (C3) & 1.0 (C2) & **0.4459 (C3)** & 1.0 (C1) \\ \cline{1-1} \cline{2-5}  & Min & 1.0 (C3) & 1.0 (C2) & **0.6453 (C1)** & 0.9784 (C1) \\ \cline{1-1} \cline{2-5}  & Mix & 1.0 (C3) & 1.0 (C2) & **0.7107 (C4)** & 0.5216 (C1) \\ \hline \end{tabular}
\end{table} 
Table 2: Performance results of different classifiers using simple and weighted PCPs combined with LLE and UMAP reduction algorithms. The number in parentheses indicates the dominant cluster for each mode. As we can see, the experiments reveal distinct performance differences among the various combinations of PCP representations and clustering algorithms. Binary PCP, particularly when used with the Agglomerative and K-Means algorithms, achieved the most promising results, successfully identifying the four target clusters while maintaining over 60% purity across all modes. This was in contrast to other representations, such as the UMAP projection of Simple PCP (Simple+UMAP), which also succeeded in identifying the four clusters but showed diminished performance, particularly with the Dorian mode, for which it achieved only 47% purity. The Binary PCP representation appears to enhance mode separability, likely due to its simplified representation, which focuses solely on the presence or absence of pitch classes. This binary simplification effectively removes extraneous details that may otherwise obscure modal patterns, making it particularly well suited for clustering algorithms such as Agglomerative and K-Means, which rely on clear, distinguishable groupings. Consequently, these two methods consistently performed better in clustering the modes with the Binary PCP than with other PCP representations. This trend is further supported by the Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI) results. The highest scores were obtained with Binary PCP and Agglomerative Clustering (NMI = 0.5976, ARI = 0.5756), followed closely by Binary PCP and K-Means (NMI = 0.5807, ARI = 0.5626). In contrast, while Simple PCP+UMAP combined with SOM managed to separate the four clusters, its performance was notably lower (NMI = 0.5165, ARI = 0.4672), aligning with its lower purity results. These findings