ArticleScientific reports2016
Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Intelligent design and synthesis of energy catalytic materials.Fundamental research · 2025Review
- Rational partitioning of spectral feature space for effective clustering of massive spectral image data.Scientific reports · 2024Article
- Phase transformation mechanism in lithium manganese nickel oxide revealed by single-crystal hard X-ray microscopy.Nature communications · 2017Article
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Authors and funding
8 authors.
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Abstract
Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd
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