The Hidden Failure of DBSCAN in High-Dimensional Data Analytics

Standard clustering algorithms like DBSCAN often fail in high-dimensional spaces due to the curse of dimensionality, where distance metrics become statistically insignificant. Understanding this sensitivity is crucial for building reliable production models that avoid arbitrary outlier classification based on unstable parameters.