Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method
By Dan Li, J. Deogun, William Spaulding, B. Shuart. 2004
229 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ahmet Arslan
- Rafael Valle
- Ibrahim Berkan Aydilek
- Jason Poulos
- Wei Ping
- Bryan Catanzaro
- Junichi Yamagishi
- Adrian La'ncucki
In Information Systems
In Signal Processing
- Ahmet Arslan
- Rafael Valle
- Ibrahim Berkan Aydilek
- Jason Poulos
- Wei Ping
- Bryan Catanzaro
- Adrian La'ncucki
- Junichi Yamagishi
Citations in the corpus
- Missing Data Imputation for Supervised Learning (2016, methodology): “6).”
- A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm (2013, methodology): “Experiments demonstrate that the fuzzy imputation algorithm yields better performance than the basic clustering algorithm [9].”