RT info:eu-repo/semantics/bookPart
T1 Robustness and Outliers
A1 García Escudero, Luis Ángel
A1 Gordaliza Ramos, Alfonso
A1 Matrán Bea, Carlos
A1 Mayo Iscar, Agustín
A1 Hennig, C.
K1 statistical applications
AB Unexpected deviations from assumed models as well as the presence of certain amounts of outlying data are common in most practical statistical applications. This fact could lead to undesirable solutions when applying non-robust statistical techniques. This is often the case in cluster analysis, too. The search for homogeneous groups with large heterogeneity between them can be spoiled due to the lack of robustness of standard clustering methods. For instance, the presence of (even few) outlying observations may result in heterogeneous clusters artificially joined together or in the detection of spurious clusters merely made up of outlying observations. In this chapter we will analyze the effects of different kinds of outlying data in cluster analysis and explore several alternative methodologies designed to avoid or minimize their undesirable effects.
PB Chapman and Hall/CRC
SN 9781466551886
YR 2015
FD 2015
LK http://uvadoc.uva.es/handle/10324/21814
UL http://uvadoc.uva.es/handle/10324/21814
LA eng
NO Handbook of Cluster Analysis. Eds.: Christian Hennig, Marina Meila, Fionn Murtagh, Roberto Rocci. Chapman and Hall/CRC, 2015. p. 653-678 (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
NO Producción Científica
DS UVaDOC
RD 24-nov-2024