Statistical analysis with missing data
Statistical analysis with missing data
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining massively incomplete data sets by conceptual reconstruction
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Software Cost Estimation with Incomplete Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Learning from Incomplete Data
A Short Note on Safest Default Missingness Mechanism Assumptions
Empirical Software Engineering
Using Multivariate Statistics (5th Edition)
Using Multivariate Statistics (5th Edition)
A new imputation method for small software project data sets
Journal of Systems and Software
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Discovery of characteristic patterns from tabular structured data including missing values
International Journal of Business Intelligence and Data Mining
Evaluating logistic regression models to estimate software project outcomes
Information and Software Technology
The optimization of success probability for software projects using genetic algorithms
Journal of Systems and Software
Learning from socio-economic characteristics of IP geo-locations for cybercrime prediction
International Journal of Business Intelligence and Data Mining
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Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data. Fortunately, Missing Data Imputation Techniques (MDITs) can be used to improve data quality. However, no one method MDIT can be used in all conditions, each method has its own context. In this paper, we introduce the MDITs to the KDD and machine learning communities by presenting the basic idea and highlighting the advantages and limitations of each method.