RT Generic
T1 Recombining partitions via unimodality tests
A1 Álvarez, Adolfo
A1 Peña, Daniel
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB In this article we propose a recombination procedure for previously split data. It is basedon the study of modes in the density of the data, since departing from unimodality canbe a sign of the presence of clusters. We develop an algorithm that integrates a splitting process inherited from the SAR algorithm (Peña et al., 2004) with unimodality tests such as the dip test proposed by Hartigan and Hartigan (1985), and finally, we use anetwork configuration to visualize the results. We show that this can be a useful tool to detect heterogeneity in the data, but limited to univariate data because of the nature of the dip test. In a second stage we discuss the use of multivariate mode detection tests to avoid dimensionality reduction techniques such as projecting multivariate data into one dimension. The results of the application of multivariate unimodality tests show that is possible to detect the cluster structure of the data, although more research can be oriented to estimate the proper fine-tuning of some parameters of the test for a given dataset or distribution.
YR 2013
FD 2013-03
LK https://hdl.handle.net/10016/18706
UL https://hdl.handle.net/10016/18706
LA eng
NO Work partially supported by Spanish Ministry of Science and Innovation, researchprojects SEJ2007-64500 and ECO2012-38442
DS e-Archivo
RD 27 jul. 2024