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Materials Selection: Selecting Appropriate Clustering Methods for Materials Science Applications of Machine Learning (Adv. Theory Simul. 12/2019)
Author(s) -
Parker Amanda J.,
Barnard Amanda S.
Publication year - 2019
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201970040
Subject(s) - cluster analysis , selection (genetic algorithm) , computer science , identification (biology) , machine learning , artificial intelligence , data mining , biology , botany
Clustering is an important method to determine the classes of materials and nanoparticles. Knowing the number and density of clusters in advance can accelerate method selection and evaluation. In article number 1900145, Amanda J. Parker and Amanda S. Barnard show how iterative label spreading (ILS) can guide the use of clustering algorithms and simplifies the identification of classes of materials using machine learning.