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Visualization of the Critical Patterns of Missing Values in Classification Data

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Advances in Visual Information Systems (VISUAL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4781))

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Abstract

The patterns of missing values are important for assessing the quality of a classification data set and the validation of classification results. The paper discusses the critical patterns of missing values in a classification data set: missing at random, uneven symmetric missing, and uneven asymmetric missing. It proposes a self-organizing maps (SOM) based cluster analysis method to visualize the patterns of missing values in classification data.

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Guoping Qiu Clement Leung Xiangyang Xue Robert Laurini

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© 2007 Springer-Verlag Berlin Heidelberg

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Wang, H., Wang, S. (2007). Visualization of the Critical Patterns of Missing Values in Classification Data. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_27

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  • DOI: https://doi.org/10.1007/978-3-540-76414-4_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76413-7

  • Online ISBN: 978-3-540-76414-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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