Stochastic Estimations of the Total Number of Classes for a Clustering having Extremely Large Samples to be Included in the Clustering Engine-Reference-Cited by-同舟云学术

Stochastic Estimations of the Total Number of Classes for a Clustering having Extremely Large Samples to be Included in the Clustering Engine

Published:2021-03-26 Issue:5 Volume:4 Page:2000301
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Adv. Theory Simul.

Author:

Utimula Keishu¹,Prayogo Genki I.¹,Nakano Kousuke²,Hongo Kenta³⁴⁵,Maezono Ryo²

Affiliation:

1. School of Materials Science JAIST Asahidai 1‐1, Nomi Ishikawa 923‐1292 Japan

2. School of Information Science JAIST Asahidai 1‐1, Nomi Ishikawa 923‐1292 Japan

3. Research Center for Advanced Computing Infrastructure JAIST Asahidai 1‐1, Nomi Ishikawa 923‐1292 Japan

4. Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305‐0047 Japan

5. PRESTO Japan Science and Technology Agency 4‐1‐8 Honcho, Kawaguchi‐shi Saitama 322‐0012 Japan

Funder

Toyota Motor Corporation

Air Force Office of Scientific Research

Precursory Research for Embryonic Science and Technology

Publisher

Wiley

Subject

Multidisciplinary,Modelling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202000301

Reference8 articles.

1. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

2. Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V–Mn–Nb Oxide System

3. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

4. Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

5. Machine‐Learning Clustering Technique Applied to Powder X‐Ray Diffraction Patterns to Distinguish Compositions of ThMn 12 ‐Type Alloys

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