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.

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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
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article

A guide to similarity measures and their data science applications

Avivit Levy, Michal Chalamish, B. Riva Shalom
5 citations
Journal Of Big Data
Time Series Analysis and Forecasting
7.43
article

A guide to similarity measures and their data science applications

Avivit Levy, Michal Chalamish, B. Riva Shalom
article en
5 citations

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 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.

Journal Of Big DataVol. 12(1)
Shenkar College of Engineering and Design (IL)
Openalex Percentile: Top 3%
Time Series Analysis and Forecasting
7.43
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