Rational partitioning of spectral feature space for effective clustering of massive spectral image data

We have successfully proposed and demonstrated a clustering method that overcomes the "needle-in-a-haystack problem" (finding minuscule important regions from massive spectral image data sets). The needle-in-a-haystack problem is of central importance in the characterization of materials since in bulk materials, the properties of a very tiny region often dominate the entire function. To solve this problem, we propose that rational partitioning of the spectral feature space in which spectra are distributed, or defining of the decision boundaries for clustering, can be performed by focusing on the discrimination limit defined by the measurement noise and partitioning the space at intervals of this limit. We verified the proposed method, applied it to actual measurement data, and succeeded in detecting tiny (~ 0.5%) important regions that were difficult for human researchers and other machine learning methods to detect in discovering unknown phases. The ability to detect these crucial regions helps in understanding materials and designing more functional materials.