Optimizing unsupervised clustering of electrochemical impedance spectra via normalization and dimensionality reduction.

Electrochemical impedance spectroscopy (EIS) is entering an exciting stage of development as machine learning–driven (ML) spectral analysis begins to complement traditional equivalent-circuit fitting and address some of its practical limitations. The appeal of a simple, fully data-driven, unsupervised workflow is clear: by operating directly on EIS spectra, it bypasses the additional modelling layers required for equivalent-circuit fitting, handcrafted feature extraction, or supervised training. Here we demonstrate that normalization and dimensionality reduction play a critical, yet previously overlooked, role in shaping the outcomes of unsupervised workflows. Using welded stainless steel as a demonstrator, we systematically evaluate combinations of normalization strategies and dimensionality-reduction pipelines. By applying internal clustering metrics and a Borda ranking, we identify an effective workflow configuration, an appropriate cluster number, and a cluster structure consistent with mechanistic expectations for the studied dataset. Mechanistically anchored linear projections further rank relative passivity across the stainless-steel passivity range via k-level clustering, while bootstrap resampling confirms high cluster stability despite the modest sample size.