English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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