Clustering performance across increasing numbers of PCA components for A1. To assess whether additional components improved separability, PCA was extended up to eight components beyond the commonly used first two or three. Clustering performance was evaluated using Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and V-measure.

Ítems similars

• K-means clustering NMI scores.
  per: Alina Troglio (13165866)
  Publicat: (2025)
• K-means clustering ARI scores.
  per: Alina Troglio (13165866)
  Publicat: (2025)
• Different feature sets are extracted through domain-agnostic dimension reduction as well as through spike approaches, using the raw waveform and computed features on the waveforms. The feature sets include methodologies, such as principal component analysis (PCA) and the features from the spike sorting approach based on shape, phase, and distribution features (SS-SPDF) by Caro-Martín et al. [11].
  per: Alina Troglio (13165866)
  Publicat: (2025)
• K-means clustering V-measure scores.
  per: Alina Troglio (13165866)
  Publicat: (2025)
• Comparison of unsupervised clustering challenges for datasets A1 and A3.
  per: Alina Troglio (13165866)
  Publicat: (2025)