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: concepts and techniques
Data mining: concepts and techniques
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Maximum Consistency of Incomplete Datavia Non-Invasive Imputation
Artificial Intelligence Review
Machine Learning
Preprocessing of Missing Values Using Robust Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Approximate Association Rule Mining
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction
Applied Intelligence
Using Association Rules for Completing Missing Data
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Missing values prediction with K2
Intelligent Data Analysis
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Data cleaning is an important step in the data mining process. Successful data mining applications require good quality data. In this paper, we propose a data cleaning technique that smoothes out a substantial amount of attribute noise and handles missing attribute values as well. Our approach is inspired by the Expectation-Maximization (EM) algorithm. It iteratively refines each attribute-value using a predictor constructed from the previously refined values (known values in the first iteration). We demonstrate the effectiveness of our technique in smoothing out attribute noise and corroborate the efficacy of our technique by showing improved classification accuracy on a number of real world data sets from UCI repository [2]. Moreover, we show that our technique can easily be adapted to fill up missing attribute-values in classification problems more effectively than other standard approaches.