@misc{oai:repo.qst.go.jp:00082961,
 author = {Hideaki, Iwasawa and Tetsuro, Ueno and Masui, Takahiko and Tajima, Setsuko and Hideaki, Iwasawa and Tetsuro, Ueno},
 month = {Jun},
 note = {Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique in modern materials science because it can directly probe electronic states, which are directly related to the physical properties of materials. Among the advanced ARPES techniques, spatially-resolved ARPES has recently attracted growing interest because of its capability to obtain local electronic information at the micro- or nano-metric length scales by utilizing a well-focused light source [1]. On the other hand, it is not trivial to analyze and understand the spatial variation of electronic states against massive datasets, typically in 4-dimensional space (energy, momentum, and two spatial axes). In this work, we present unsupervised clustering analyses based on K-means and Fuzzy-c-means methods on spatially-resolved micro-ARPES data from Y-based high-Tc cuprate superconductor YBa2Cu3O7-δ, which shows spatial inhomogeneity due to multiple surface terminations due to BaO or CuO layers on a cleavage (001) plane [2]. The performance of the clustering analyses will be demonstrated with the comparison of the conventional analysis method.
[1] Hideaki Iwasawa, Electronic Structure 2, 043001 (2020).
[2] H. Iwasawa et al., Phys. Rev. B 98, 081112(R) (2018)., The 11th New Generation in Strongly Correlated Electron Systems},
 title = {Unsupervised Clustering of Spatially-resolved ARPES Data},
 year = {2021}
}