Statistical analysis with missing data
Statistical analysis with missing data
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
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
A genetic algorithm for cluster analysis
Intelligent Data Analysis
Towards efficient imputation by nearest-neighbors: a clustering-based approach
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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The substitution of missing values, also called imputation, is an important data preparation task for data mining applications. This paper describes a nearest-neighbor method to impute missing values, showing that it can be useful for a clustering genetic algorithm. The proposed nearest-neighbor method is assessed by means of simulations performed in two datasets that are benchmarks for data mining methods: Wisconsin Breast Cancer and Congressional Voting Records. The efficacy of the proposed approach is evaluated both in prediction and clustering scenarios. Empirical results show that the employed imputation method is a suitable data preparation tool.