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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Missing Value Imputation Based on Data Clustering
verfasst von : Shichao Zhang, Jilian Zhang, Xiaofeng Zhu, Yongsong Qin, Chengqi Zhang
Erschienen in: Transactions on Computational Science I
Verlag: Springer Berlin Heidelberg
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We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance
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with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to the instance
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using a kernel-based method. Specifically, we first divide the dataset (including the instances with missing values) into clusters. Next, missing values of an instance
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are patched up with the plausible values generated from
A
’s cluster. Extensive experiments show the effectiveness of the proposed method in missing value imputation task.