Une nouvelle approche de compl\'etion des valeurs manquantes dans les bases de donn\'ees

TL;DR

This paper introduces a novel method for imputing missing data in datasets by leveraging association rules, reducing conflicts, and improving accuracy in data completion tasks.

Contribution

It presents a new approach combining association rules with missing data imputation, introducing a robustness metric to select the most reliable rules.

Findings

Reduces conflicts during data completion

Achieves high accuracy in missing value imputation

Validated on benchmark datasets

Abstract

When tackling real-life datasets, it is common to face the existence of scrambled missing values within data. Considered as 'dirty data', usually it is removed during a pre-processing step. Starting from the fact that 'making up this missing data is better than throwing out it away', we present a new approach trying to complete missing data. The main singularity of the introduced approach is that it sheds light on a fruitful synergy between generic basis of association rules and the topic of missing values handling. In fact, beyond interesting compactness rate, such generic association rules make it possible to get a considerable reduction of conflicts during the completion step. A new metric called 'Robustness' is also introduced, and aims to select the robust association rule for the completion of a missing value whenever a conflict appears. Carried out experiments on benchmark datasets confirm the soundness of our approach. Thus, it reduces conflict during the completion step while offering a high percentage of correct completion accuracy.