Name
Abstract | ||
|---|---|---|
The issue of missing values (MVs) has appeared widely in real-world datasets and hindered the use of many statistical or machine learning algorithms for data analytics due to their incompetence in handling incomplete datasets. To address this issue, several MV imputation algorithms have been developed. However, these approaches do not perform well when most of the incomplete tuples are clustered w... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TKDE.2018.2822662 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Diabetes,Machine learning algorithms,Clustering algorithms,Heart,Approximation algorithms,Air quality,Diseases | Approximation algorithm,Data mining,Heuristic,Data analysis,Computer science,Tuple,Artificial intelligence,Missing data,Imputation (statistics),Cluster analysis,Optimization problem,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 1 | 1041-4347 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Ma | 1 | 370 | 29.93 |
| Yu Gu | 2 | 201 | 34.98 |
| Wang-Chien Lee | 3 | 5765 | 346.32 |
| Ge YU | 4 | 1313 | 175.88 |