Pythagorean Centrality for Data Selection

TL;DR

This paper explores Pythagorean centrality measures, their historical evolution, geometric interpretations, and practical use cases, providing guidance on selecting appropriate means for data-driven predictions.

Contribution

It offers a comprehensive overview of arithmetic, geometric, and harmonic means, highlighting their differences, similarities, and application contexts for data selection.

Findings

Historical development of means

Geometrical interpretations of the means

Guidelines for choosing the appropriate mean

Abstract

This paper provides an overview of the Pythagorean centrality measures, which are the arithmetic, geometric, and harmonic means. Both the evolution of their meaning through history and their geometrical interpretation are outlined. Relevant examples of use cases for each of them are introduced, spanning a variety of areas of knowledge. Their differences and similarities are explored. Finally, the issue of which mean to use in different situations in order to make advantageous predictions is addressed. Keywords: central tendency, Pythagorean means, arithmetic mean, geometric mean, harmonic mean, data selection