Abstract
We describe possibilities how the well-known linkage techniques for hierarchical clustering can be modified to consider the problem of missing values in dissimilarity data. The resulting MVL (Missing Values Linkage) approach is presented and compared with a least squares-based penalty algorithm. In an example, a distance table of selected European cities is used to demonstrate features of the MVL approach. Randomly chosen distances are assumed missing, the non-missing distances are superimposed with random error, and different subsets of cities are taken into consideration.
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© 1992 Springer-Verlag Berlin · Heidelberg
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Schader, M., Gaul, W. (1992). The MVL (Missing Values Linkage) Approach for Hierarchical Classification when Data are Incomplete. In: Schader, M. (eds) Analyzing and Modeling Data and Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-46757-8_12
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DOI: https://doi.org/10.1007/978-3-642-46757-8_12
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