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Missing Data in Hierarchical Classification of Variables — a Simulation Study

  • Ana Lorga da Silva
  • Helena Bacelar-Nicolau
  • Gilbert Saporta
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Here we develop from a first work the effect of missing data in hierarchical classification of variables according to the following factors: amount of missing data, imputation techniques, similarity coefficient, and aggregation criterion. We have used two methods of imputation, a regression method using an OLS method and an EM algorithm. For the similarity matrices we have used the basic affinity coefficient and the Pearson’s correlation coefficient. As aggregation criteria we apply average linkage, single linkage and complete linkage methods. To compare the structure of the hierarchical classifications the Spearman’s coefficient between the associated ultrametrics has been used. We present here simulation experiments in five multivariate normal cases.

Keywords

Imputation Method Average Linkage Single Linkage Complete Linkage Hierarchical Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ana Lorga da Silva
    • 1
  • Helena Bacelar-Nicolau
    • 2
  • Gilbert Saporta
    • 3
  1. 1.Department of MathematicsISEG, Tecnic UniversityLisbonPortugal
  2. 2.FPCE LEAD — Lisbon UniversityLisbonPortugal
  3. 3.Statistics DepartmentCNAMParisFrance

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