Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Statistical analysis with missing data
Statistical analysis with missing data
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Journal of Intelligent Information Systems
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Learning missing values from summary constraints
ACM SIGKDD Explorations Newsletter
Screening and interpreting multi-item associations based on log-linear modeling
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
OLAP over uncertain and imprecise data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
OLAP over uncertain and imprecise data
The VLDB Journal — The International Journal on Very Large Data Bases
Imputing time series data by regional-gradient-guided bootstrapping algorithm
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Journal of Intelligent Information Systems
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The presence of missing or incomplete data is a commonplace in large real-word databases.In this paper, we study the problem of missing values which occur at the measure dimension of data cube.We propose a two-part mixture model, which combines the logistic model and loglinear model together, to predict and impute the missing values. The logistic model here is applied to predict missing positions while the loglinear model is applied to compute the estimation. Experimental results on real datasets and synthetic datasets are presented.