1 research outputs found
Une nouvelle approche de compl\'etion des valeurs manquantes dans les bases de donn\'ees
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.Comment: in Frenc