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International Conference on Knowledge Science, Engineering and Management

KSEM 2011: Knowledge Science, Engineering and Management pp 414–424Cite as

Imputing Missing Values for Mixed Numeric and Categorical Attributes Based on Incomplete Data Hierarchical Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7091))

Abstract

Missing data imputation is a key issue of data pre-processing in data mining field. Though there are many methods for missing value imputation, almost each of these imputation methods has its limitation and is designed for either numeric attributes or categorical attributes. This paper presents IMIC, a new missing value Imputation method for Mixed numeric and categorical attributes based on Incomplete data hierarchical clustering after the introduction of a new concept Incomplete Set Mixed Feature Vector (ISMFV). The effect of the new method is valuated through the comparison experiment using 3 real data sets from UCI.

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Author information

Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, P.R. China

    Xiaodong Feng, Sen Wu & Yanchi Liu

Authors
  1. Xiaodong Feng
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  2. Sen Wu
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  3. Yanchi Liu
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Editor information

Editors and Affiliations

  1. Management Science and Information Systems Department, Rutgers, the State University of New Jersey, 1, Washington Park, 07102, Newark, NJ, USA

    Hui Xiong

  2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    W. B. Lee

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© 2011 Springer-Verlag Berlin Heidelberg

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Feng, X., Wu, S., Liu, Y. (2011). Imputing Missing Values for Mixed Numeric and Categorical Attributes Based on Incomplete Data Hierarchical Clustering. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_37

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  • DOI: https://doi.org/10.1007/978-3-642-25975-3_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25974-6

  • Online ISBN: 978-3-642-25975-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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