Directional Multivariate Ranking

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining ◽

10.1145/3394486.3403051 ◽

2020 ◽

Author(s):

Nan Wang ◽

Hongning Wang

Keyword(s):

Multivariate Ranking

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A Multivariate Solution of the Multivariate Ranking and Selection Problem

10.21236/ada082035 ◽

1980 ◽

Author(s):

Edward J. Dudewicz ◽

Vidya S. Taneja

Keyword(s):

Ranking And Selection ◽

Selection Problem ◽

Multivariate Ranking

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Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

Symmetry ◽

10.3390/sym12122055 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2055

Author(s):

Marta Bystrzanowska ◽

Marek Tobiszewski

Keyword(s):

Green Chemistry ◽

Principal Component ◽

Sustainable Chemistry ◽

Structure Property ◽

Sustainable Solutions ◽

Structure Property Relationships ◽

Quantitative Structure Property Relationships ◽

Multivariate Ranking ◽

Selection Of

In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.

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