A guide to similarity measures and their data science applications
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 professionals. 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.
Authors
- Avivit Levy (ORCID: https://orcid.org/0000-0002-1686-0094)
- Michal Chalamish (ORCID: https://orcid.org/0000-0001-9613-713X)
- B. Riva Shalom
Institutions
- Shenkar College of Engineering and Design (IL)
Publication Details
- Journal
- Journal Of Big Data
- Published
- 2025-07-26
- DOI
- https://doi.org/10.1186/s40537-025-01227-1
- Citations
- 5
- Primary Topic
- Time Series Analysis and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 7.43