Abstract
A data depth depth(y, χ) measures how deep a point y lies in a set χ. The corresponding α-trimmed regions Dα(χ) = y : depth(y,χ) ≤ α are monotonely decreasing with α, that is a α > β implies Dα ⊂ Dβ. We introduce clustering procedures based on weighted averages of volumes of α-trimmed regions.The hypervolume method turns out to be a special case of these procedures.We investigate the performance in a simulation study.
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Hoberg, R. (2000). Cluster Analysis Based on Data Depth. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_2
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DOI: https://doi.org/10.1007/978-3-642-59789-3_2
Publisher Name: Springer, Berlin, Heidelberg
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