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.Exp erimental results on real datasets and synthetic datasets are presented. © 2002 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Wu, X., & Barbará, D. (2002). Modeling and imputation of large incomplete multidimensional datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2454 LNCS, pp. 286–295). Springer Verlag. https://doi.org/10.1007/3-540-46145-0_28
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