Statistical analysis with missing data
Statistical analysis with missing data
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Data mining with neural networks: solving business problems from application development to decision support
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data preparation for data mining
Data preparation for data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: concepts and techniques
Data mining: concepts and techniques
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A genetic algorithm for cluster analysis
Intelligent Data Analysis
Missing values prediction with K2
Intelligent Data Analysis
International Journal of Hybrid Intelligent Systems
Hi-index | 0.00 |
Missing values are a critical problem in data mining applications. The substitution of these values, also called imputation, can be performed by several methods. This work describes the application of an optimized version of the Bayesian Algorithm K2 as an imputation tool for a clustering genetic algorithm. The resulting hybrid system is assessed by means of simulations in five benchmark datasets. The obtained results indicate that the proposed imputation method is a suitable data preparation tool for the employed clustering genetic algorithm.