This book presents a systematic review of multidimensional normalization methods and addresses problems frequently encountered when using various methods and ways to eliminate them.
The invariant properties of the linear normalization methods presented here can be used to eliminate simple problems and avoid obvious errors when choosing a normalization method. The book introduces valuable, novel techniques for the multistep normalization of multidimensional data. One of these methods involves inverting the normalized values of cost attributes into profit attributes based on the reverse sorting algorithm (ReS algorithm). Another approach presented is the IZ method, which addresses the issue of shift in normalized attribute values. Additionally, a new method for normalizing the decision matrix is proposed, called the MS method, which ensures the equalization of average values and variances of attributes.
Featuring numerous illustrative examples throughout, the book helps readers to understand what difficulties can arise in multidimensional normalization, what to expect from such problems, and how to solve them. It is intended for academics and professionals in various areas of data science, computing in mathematics, and statistics, as well as decision-making and operations.
| ISBN: | 9783031338397 |
| Publication date: | 27th July 2024 |
| Author: | Irik Z Mukhametzyanov |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 292 pages |
| Series: | International Series in Operations Research & Management Science |
| Genres: |
Operational research Management decision making Mathematical theory of computation |
This book presents a systematic review of multidimensional normalization methods and addresses problems frequently encountered when using various methods and ways to eliminate them.
The invariant properties of the linear normalization methods presented here can be used to eliminate simple problems and avoid obvious errors when choosing a normalization method. The book introduces valuable, novel techniques for the multistep normalization of multidimensional data. One of these methods involves inverting the normalized values of cost attributes into profit attributes based on the reverse sorting algorithm (ReS algorithm). Another approach presented is the IZ method, which addresses the issue of shift in normalized attribute values. Additionally, a new method for normalizing the decision matrix is proposed, called the MS method, which ensures the equalization of average values and variances of attributes.
Featuring numerous illustrative examples throughout, the book helps readers to understand what difficulties can arise in multidimensional normalization, what to expect from such problems, and how to solve them. It is intended for academics and professionals in various areas of data science, computing in mathematics, and statistics, as well as decision-making and operations.
Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems features in the following genres: Operational research, Management decision making, Mathematical theory of computation
Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems is available in Paperback
Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems was written by Irik Z Mukhametzyanov and published by Springer an imprint of Springer International Publishing
Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems has 292 pages
Yes it is part of International Series in Operations Research & Management Science series
£116.99