Can I read Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans) on EtoBox?

Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans) by Alfred Ultsch; Jörn Lötsch is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans) about?

## Background Data transformations are commonly used in bioinformatics data processing in the context of data projection and clustering. The most used Euclidean metric is not scale invariant and therefore occasionally inappropriate for complex, e.g., multimodal distributed variables and may negatively affect the results of cluster analysis. Specifically, the squaring function in the definition of the Euclidean distance as the square root of the sum of squared differences between data points has the consequence that the value 1 implicitly defines a limit for distances within clusters versus distances between (inter-) clusters. ## Methods The Euclidean distances within a standard normal distribution (N(0,1)) follow a N(0,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{2}$$\end{document}2) distribution. The EDO-transformation of a variable X is proposed as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage

Who reads Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans)?

Author

Alfred Ultsch; Jörn Lötsch

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)