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Missing Value Imputation Based on Data Clustering

  • Shichao Zhang
  • Jilian Zhang
  • Xiaofeng Zhu
  • Yongsong Qin
  • Chengqi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4750)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shichao Zhang
    • 1
  • Jilian Zhang
    • 2
  • Xiaofeng Zhu
    • 1
  • Yongsong Qin
    • 1
  • Chengqi Zhang
    • 3
  1. 1.Department of Computer ScienceGuangxi Normal UniversityGuilinChina
  2. 2.School of Information SystemsSingapore Management UniversitySingapore
  3. 3.Faculty of Information TechnologyUniversity of Technology SydneyAustralia

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