Missing Value Imputation Based on Data Clustering
Missing Value Imputation Based on Data Clustering
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DOI：
10.1007/978-3-540-79299-4_7
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发表时间：
2008
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影响因子：
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通讯作者：
Shichao Zhang;Jilian Zhang;Xiaofeng Zhu;Yongsong Qin;Chengqi Zhang
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文献类型：
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作者：
Shichao Zhang;Jilian Zhang;Xiaofeng Zhu;Yongsong Qin;Chengqi Zhang
提出了一种基于聚类的非参数缺失值插补方法CMI（Questioning-based Missing Value Imputation），用于处理目标属性中的缺失值。在我们的方法中，我们使用基于核的方法将实例A的缺失值与从不包含缺失值的实例中的数据生成的合理值进行估算，并且与实例A最相似。具体来说，我们首先将数据集（包括缺失值的实例）划分为聚类。接下来，实例A的缺失值被从A的集群中生成的合理值修补。大量的实验表明，该方法在缺失值填补任务的有效性。
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 A 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 A 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 A 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.