Title: Selecting among multi-mode partitioning models of different complexities: A comparison of four model selection criteria
Authors: Schepers, Jan ×
Ceulemans, Eva
Van Mechelen, Iven #
Issue Date: Jun-2008
Publisher: Published for the Classification Society of North America by Springer-Verlag New York
Series Title: Journal of Classification vol:25 issue:1 pages:67-85
Abstract: Multi-mode partitioning models for N-way N-mode data reduce each of
the N modes in the data to a small number of clusters that are mutually exclusive.
Given a specific N-mode data set, one may wonder which multi-mode partitioning
model (i.e., with which numbers of clusters for each mode) yields the most useful
description of this data set and should therefore be selected. In this paper, we address
this issue by investigating four possible model selection heuristics: multi-mode extensions
of Calinski and Harabasz’s (1974) and Kaufman and Rousseeuw’s (1990)
indices for one-mode k-means clustering and multi-mode partitioning versions of
Timmerman and Kiers’s (2000) DIFFIT and Ceulemans and Kiers’s (2006) numerical
convex hull based model selection heuristic for three-mode principal component
analysis. The performance of these four heuristics is systematically compared in a
simulation study, which shows that the DIFFIT and numerical convex hull heuristics
perform satisfactory in the two-mode partitioning case and very good in the threemode
partitioning case.
ISSN: 0176-4268
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Quantitative Psychology and Individual Differences
Methodology of Educational Sciences
× corresponding author
# (joint) last author

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