A Guide to Similarity Measures

TL;DR

This paper provides a comprehensive overview of various similarity measures, explaining their formulas, motivations, and design principles to assist both novices and experts in selecting and understanding appropriate measures for data analysis tasks.

Contribution

It offers a detailed, accessible guide to prevalent similarity measures, including their formulas, motivations, and design principles, bridging knowledge for non-experts and experts.

Findings

Extensive collection of similarity measures with explanations

Guidance on choosing appropriate measures for different tasks

Insights into designing new similarity measures

Abstract

Similarity measures play a central role in various data science application domains for a wide assortment of tasks. This guide describes a comprehensive set of prevalent similarity measures to serve both non-experts and professional. Non-experts that wish to understand the motivation for a measure as well as how to use it may find a friendly and detailed exposition of the formulas of the measures, whereas experts may find a glance to the principles of designing similarity measures and ideas for a better way to measure similarity for their desired task in a given application domain.