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Examining large databases: A chemometric approach using principal component analysis
Author(s) -
Meglen Robert R.
Publication year - 1991
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180050305
Subject(s) - principal component analysis , exploratory data analysis , multivariate statistics , computer science , chemometrics , data mining , exploratory analysis , component (thermodynamics) , graphical display , multivariate analysis , database , artificial intelligence , data science , machine learning , thermodynamics , physics , computer graphics (images)
Principal component analysis is used to examine large multivariate databases. The graphical approach to exploratory data analysis is described and illustrated with a single example of chemical composition data obtained on environmental dust particles. While the graphical approach to exploratory data analysis has certain advantages over the numerical procedures, the empirical approach described here should be viewed as complementary to the more robust treatments that statistical methodologies afford.