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

A large number of scientific researches and industrial applications commonly suffer from missing data. Some inappropriate techniques of missing value treatment compromise data quality, which detrimentally influences the knowledge discovery. In this paper, we propose a missing data completion method named CBGMI. Firstly, it separates the nonmissing data instances into several clusters by excluding the missing-valued entries. Then, it utilizes the entropy of the proximal category for each incomplete instance in terms of the similarity metric based on gray relational analysis. Experiments on UCI datasets and aerospace datasets demonstrate that the superiority of our algorithm to other approaches on validity.

Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field