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Exploratory Data Analysis (EDA) is the tedious activity of interactively analyzing a dataset to extract insights. Many approaches aiming at supporting EDA were recently proposed. They all rely on interestingness measures to score the importance of insights. This paper surveys and categorizes the different interestingness measures proposed in the literature for approaches aiming at automating EDA. The lessons learned from this survey allow to point out promising research directions.

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Interestingness Measures for Exploratory Data Analysis: a Survey

  • 会议论文

  • 发表日期：

    2025年

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  • Alexandre Chanson,
  • Nicolas Labroche,
  • Patrick Marcel,
  • Verónika Perlata,
  • Panos Vassiliadis

摘要

Exploratory Data Analysis (EDA) is the tedious activity of interactively analyzing a dataset to extract insights. Many approaches aiming at supporting EDA were recently proposed. They all rely on interestingness measures to score the importance of insights. This paper surveys and categorizes the different interestingness measures proposed in the literature for approaches aiming at automating EDA. The lessons learned from this survey allow to point out promising research directions.